Pandas Profiling Report

Dataset statistics

Number of variables	22
Number of observations	5269
Missing cells	51983
Missing cells (%)	44.8%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	905.7 KiB
Average record size in memory	176.0 B

Variable types

Text	10
Categorical	6
Numeric	4
Boolean	1
DateTime	1

Alerts

`n_subscribers` is highly overall correlated with `n_reviews`	High correlation
`price` is highly overall correlated with `mooc` and 1 other fields	High correlation
`n_reviews` is highly overall correlated with `n_subscribers` and 1 other fields	High correlation
`n_lectures` is highly overall correlated with `mooc`	High correlation
`mooc` is highly overall correlated with `price` and 8 other fields	High correlation
`modality` is highly overall correlated with `mooc` and 1 other fields	High correlation
`level` is highly overall correlated with `mooc` and 1 other fields	High correlation
`subject` is highly overall correlated with `mooc` and 1 other fields	High correlation
`language` is highly overall correlated with `mooc` and 2 other fields	High correlation
`subtitles` is highly overall correlated with `mooc` and 2 other fields	High correlation
`paid` is highly overall correlated with `price` and 6 other fields	High correlation
`modality` is highly imbalanced (67.4%)	Imbalance
`language` is highly imbalanced (72.0%)	Imbalance
`subtitles` is highly imbalanced (70.1%)	Imbalance
`institution` has 3672 (69.7%) missing values	Missing
`id` has 974 (18.5%) missing values	Missing
`summary` has 4348 (82.5%) missing values	Missing
`n_subscribers` has 743 (14.1%) missing values	Missing
`modality` has 4295 (81.5%) missing values	Missing
`instructors` has 4298 (81.6%) missing values	Missing
`level` has 623 (11.8%) missing values	Missing
`subject` has 623 (11.8%) missing values	Missing
`language` has 4295 (81.5%) missing values	Missing
`subtitles` has 4298 (81.6%) missing values	Missing
`effort` has 4295 (81.5%) missing values	Missing
`duration` has 623 (11.8%) missing values	Missing
`price` has 4295 (81.5%) missing values	Missing
`description` has 4335 (82.3%) missing values	Missing
`curriculum` has 4852 (92.1%) missing values	Missing
`paid` has 623 (11.8%) missing values	Missing
`n_reviews` has 1597 (30.3%) missing values	Missing
`n_lectures` has 1597 (30.3%) missing values	Missing
`published` has 1597 (30.3%) missing values	Missing
`n_subscribers` is highly skewed (γ1 = 23.31018807)	Skewed
`url` has unique values	Unique
`n_subscribers` has 65 (1.2%) zeros	Zeros
`n_reviews` has 284 (5.4%) zeros	Zeros

Reproduction

Analysis started	2023-06-15 11:20:57.559410
Analysis finished	2023-06-15 11:21:21.371152
Duration	23.81 seconds
Software version	ydata-profiling vv4.2.0
Download configuration	config.json

title
Text

Distinct	5240
Distinct (%)	99.4%
Missing	0
Missing (%)	0.0%
Memory size	41.3 KiB

Length

Max length	243
Median length	91
Mean length	43.781173
Min length	6

Characters and Unicode

Total characters	230683
Distinct characters	528
Distinct categories	18 ?
Distinct scripts	10 ?
Distinct blocks	15 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	5215 ?
Unique (%)	99.0%

Sample

1st row	Machine Learning
2nd row	Indigenous Canada
3rd row	The Science of Well-Being
4th row	Technical Support Fundamentals
5th row	Become a CBRS Certified Professional Installer by Google

Value	Count	Frequency (%)
	1228	3.5%
to	1027	2.9%
and	874	2.5%
for	739	2.1%
the	722	2.1%
a	571	1.6%
learn	511	1.5%
in	504	1.4%
with	451	1.3%
trading	316	0.9%
Other values (5552)	28076	80.2%

Most occurring characters

Value	Count	Frequency (%)
	29897	13.0%
e	18578	8.1%
n	15249	6.6%
a	14385	6.2%
o	14190	6.2%
i	14118	6.1%
t	13094	5.7%
r	12771	5.5%
s	10709	4.6%
l	6687	2.9%
Other values (518)	81005	35.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	164261	71.2%
Space Separator	29916	13.0%
Uppercase Letter	28722	12.5%
Other Punctuation	3003	1.3%
Decimal Number	1971	0.9%
Other Letter	1154	0.5%
Dash Punctuation	1054	0.5%
Close Punctuation	197	0.1%
Open Punctuation	196	0.1%
Math Symbol	101	< 0.1%
Other values (8)	108	< 0.1%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
ل	52	4.5%
ا	46	4.0%
و	24	2.1%
で	22	1.9%
ي	22	1.9%
م	20	1.7%
ر	19	1.6%
る	19	1.6%
ス	19	1.6%
の	18	1.6%
Other values (330)	893	77.4%

Lowercase Letter

Value	Count	Frequency (%)
e	18578	11.3%
n	15249	9.3%
a	14385	8.8%
o	14190	8.6%
i	14118	8.6%
t	13094	8.0%
r	12771	7.8%
s	10709	6.5%
l	6687	4.1%
c	6233	3.8%
Other values (61)	38247	23.3%

Uppercase Letter

Value	Count	Frequency (%)
S	2694	9.4%
C	2527	8.8%
P	2408	8.4%
T	2147	7.5%
A	2055	7.2%
B	1779	6.2%
M	1615	5.6%
L	1557	5.4%
F	1535	5.3%
I	1473	5.1%
Other values (29)	8932	31.1%

Other Punctuation

Value	Count	Frequency (%)
:	1348	44.9%
,	449	15.0%
&	312	10.4%
!	273	9.1%
.	242	8.1%
'	150	5.0%
/	114	3.8%
#	34	1.1%
"	24	0.8%
?	19	0.6%
Other values (8)	38	1.3%

Decimal Number

Value	Count	Frequency (%)
1	496	25.2%
2	337	17.1%
0	322	16.3%
3	207	10.5%
5	204	10.4%
4	140	7.1%
7	85	4.3%
6	77	3.9%
8	58	2.9%
9	40	2.0%
Other values (3)	5	0.3%

Nonspacing Mark

Value	Count	Frequency (%)
่	5	27.8%
้	2	11.1%
ั	2	11.1%
ّ	2	11.1%
ิ	2	11.1%
ื	2	11.1%
ี	1	5.6%
๊	1	5.6%
️	1	5.6%

Math Symbol

Value	Count	Frequency (%)
+	49	48.5%
\|	44	43.6%
>	4	4.0%
｜	1	1.0%
=	1	1.0%
≪	1	1.0%
≫	1	1.0%

Close Punctuation

Value	Count	Frequency (%)
)	185	93.9%
」	3	1.5%
】	3	1.5%
）	3	1.5%
]	2	1.0%
}	1	0.5%

Open Punctuation

Value	Count	Frequency (%)
(	184	93.9%
「	3	1.5%
【	3	1.5%
（	3	1.5%
[	2	1.0%
{	1	0.5%

Dash Punctuation

Value	Count	Frequency (%)
-	1021	96.9%
–	30	2.8%
〜	2	0.2%
—	1	0.1%

Space Separator

Value	Count	Frequency (%)
	29897	99.9%
	12	< 0.1%
	7	< 0.1%

Other Symbol

Value	Count	Frequency (%)
®	17	85.0%
™	2	10.0%
✔	1	5.0%

Letter Number

Value	Count	Frequency (%)
Ⅰ	2	50.0%
Ⅱ	1	25.0%
Ⅲ	1	25.0%

Modifier Symbol

Value	Count	Frequency (%)
`	1	50.0%
´	1	50.0%

Final Punctuation

Value	Count	Frequency (%)
’	25	100.0%

Modifier Letter

Value	Count	Frequency (%)
ー	23	100.0%

Format

Value	Count	Frequency (%)
	11	100.0%

Control

Value	Count	Frequency (%)
	5	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	192736	83.6%
Common	36524	15.8%
Han	374	0.2%
Arabic	318	0.1%
Cyrillic	252	0.1%
Hiragana	203	0.1%
Katakana	196	0.1%
Thai	68	< 0.1%
Hangul	9	< 0.1%
Inherited	3	< 0.1%

Most frequent character per script

Han

Value	Count	Frequency (%)
学	15	4.0%
入	8	2.1%
資	7	1.9%
日	7	1.9%
門	7	1.9%
心	6	1.6%
基	6	1.6%
初	5	1.3%
分	5	1.3%
与	5	1.3%
Other values (181)	303	81.0%

Latin

Value	Count	Frequency (%)
e	18578	9.6%
n	15249	7.9%
a	14385	7.5%
o	14190	7.4%
i	14118	7.3%
t	13094	6.8%
r	12771	6.6%
s	10709	5.6%
l	6687	3.5%
c	6233	3.2%
Other values (70)	66722	34.6%

Common

Value	Count	Frequency (%)
	29897	81.9%
:	1348	3.7%
-	1021	2.8%
1	496	1.4%
,	449	1.2%
2	337	0.9%
0	322	0.9%
&	312	0.9%
!	273	0.7%
.	242	0.7%
Other values (56)	1827	5.0%

Katakana

Value	Count	Frequency (%)
ス	19	9.7%
ン	17	8.7%
タ	14	7.1%
ト	13	6.6%
ギ	10	5.1%
ッ	10	5.1%
レ	8	4.1%
リ	7	3.6%
ル	7	3.6%
イ	6	3.1%
Other values (37)	85	43.4%

Hiragana

Value	Count	Frequency (%)
で	22	10.8%
る	19	9.4%
の	18	8.9%
を	12	5.9%
に	12	5.9%
も	8	3.9%
な	7	3.4%
き	7	3.4%
う	7	3.4%
め	6	3.0%
Other values (32)	85	41.9%

Cyrillic

Value	Count	Frequency (%)
а	33	13.1%
о	23	9.1%
н	22	8.7%
и	20	7.9%
р	16	6.3%
в	14	5.6%
т	14	5.6%
е	14	5.6%
л	10	4.0%
с	9	3.6%
Other values (24)	77	30.6%

Thai

Value	Count	Frequency (%)
อ	7	10.3%
า	6	8.8%
่	5	7.4%
น	5	7.4%
ง	4	5.9%
ร	4	5.9%
ฟ	3	4.4%
ย	3	4.4%
้	2	2.9%
ช	2	2.9%
Other values (19)	27	39.7%

Arabic

Value	Count	Frequency (%)
ل	52	16.4%
ا	46	14.5%
و	24	7.5%
ي	22	6.9%
م	20	6.3%
ر	19	6.0%
س	15	4.7%
ة	14	4.4%
ت	14	4.4%
ب	14	4.4%
Other values (18)	78	24.5%

Hangul

Value	Count	Frequency (%)
바	1	11.1%
캔	1	11.1%
로	1	11.1%
콘	1	11.1%
기	1	11.1%
들	1	11.1%
만	1	11.1%
츠	1	11.1%
텐	1	11.1%

Inherited

Value	Count	Frequency (%)
ّ	2	66.7%
️	1	33.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	228562	99.1%
None	599	0.3%
CJK	374	0.2%
Arabic	320	0.1%
Cyrillic	252	0.1%
Katakana	219	0.1%
Hiragana	203	0.1%
Thai	68	< 0.1%
Punctuation	67	< 0.1%
Hangul	9	< 0.1%
Other values (5)	10	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	29897	13.1%
e	18578	8.1%
n	15249	6.7%
a	14385	6.3%
o	14190	6.2%
i	14118	6.2%
t	13094	5.7%
r	12771	5.6%
s	10709	4.7%
l	6687	2.9%
Other values (79)	78884	34.5%

None

Value	Count	Frequency (%)
ó	177	29.5%
á	76	12.7%
í	62	10.4%
é	54	9.0%
ñ	31	5.2%
ã	25	4.2%
ç	24	4.0%
®	17	2.8%
ú	15	2.5%
	12	2.0%
Other values (35)	106	17.7%

Arabic

Value	Count	Frequency (%)
ل	52	16.2%
ا	46	14.4%
و	24	7.5%
ي	22	6.9%
م	20	6.2%
ر	19	5.9%
س	15	4.7%
ة	14	4.4%
ت	14	4.4%
ب	14	4.4%
Other values (19)	80	25.0%

Cyrillic

Value	Count	Frequency (%)
а	33	13.1%
о	23	9.1%
н	22	8.7%
и	20	7.9%
р	16	6.3%
в	14	5.6%
т	14	5.6%
е	14	5.6%
л	10	4.0%
с	9	3.6%
Other values (24)	77	30.6%

Punctuation

Value	Count	Frequency (%)
–	30	44.8%
’	25	37.3%
	11	16.4%
—	1	1.5%

Katakana

Value	Count	Frequency (%)
ー	23	10.5%
ス	19	8.7%
ン	17	7.8%
タ	14	6.4%
ト	13	5.9%
ギ	10	4.6%
ッ	10	4.6%
レ	8	3.7%
リ	7	3.2%
ル	7	3.2%
Other values (38)	91	41.6%

Hiragana

Value	Count	Frequency (%)
で	22	10.8%
る	19	9.4%
の	18	8.9%
を	12	5.9%
に	12	5.9%
も	8	3.9%
な	7	3.4%
き	7	3.4%
う	7	3.4%
め	6	3.0%
Other values (32)	85	41.9%

CJK

Value	Count	Frequency (%)
学	15	4.0%
入	8	2.1%
資	7	1.9%
日	7	1.9%
門	7	1.9%
心	6	1.6%
基	6	1.6%
初	5	1.3%
分	5	1.3%
与	5	1.3%
Other values (181)	303	81.0%

Thai

Value	Count	Frequency (%)
อ	7	10.3%
า	6	8.8%
่	5	7.4%
น	5	7.4%
ง	4	5.9%
ร	4	5.9%
ฟ	3	4.4%
ย	3	4.4%
้	2	2.9%
ช	2	2.9%
Other values (19)	27	39.7%

Number Forms

Value	Count	Frequency (%)
Ⅰ	2	50.0%
Ⅱ	1	25.0%
Ⅲ	1	25.0%

Letterlike Symbols

Value	Count	Frequency (%)
™	2	100.0%

Dingbats

Value	Count	Frequency (%)
✔	1	100.0%

VS

Value	Count	Frequency (%)
️	1	100.0%

Math Operators

Value	Count	Frequency (%)
≪	1	50.0%
≫	1	50.0%

Hangul

Value	Count	Frequency (%)
바	1	11.1%
캔	1	11.1%
로	1	11.1%
콘	1	11.1%
기	1	11.1%
들	1	11.1%
만	1	11.1%
츠	1	11.1%
텐	1	11.1%

institution
Text

Distinct	230
Distinct (%)	14.4%
Missing	3672
Missing (%)	69.7%
Memory size	41.3 KiB

Length

Max length	104
Median length	47
Mean length	24.823419
Min length	3

Characters and Unicode

Total characters	39643
Distinct characters	72
Distinct categories	11 ?
Distinct scripts	2 ?
Distinct blocks	3 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	61 ?
Unique (%)	3.8%

Sample

1st row	Stanford University
2nd row	University of Alberta
3rd row	Yale University
4th row	Google
5th row	Google - Spectrum Sharing

Value	Count	Frequency (%)
university	922	18.2%
of	634	12.5%
the	185	3.6%
de	152	3.0%
technology	127	2.5%
harvard	103	2.0%
institute	96	1.9%
universidad	90	1.8%
california	74	1.5%
college	71	1.4%
Other values (326)	2623	51.7%

Most occurring characters

Value	Count	Frequency (%)
i	3804	9.6%
e	3547	8.9%
	3488	8.8%
n	3147	7.9%
a	2281	5.8%
r	2272	5.7%
o	2269	5.7%
t	2226	5.6%
s	2024	5.1%
v	1429	3.6%
Other values (62)	13156	33.2%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	31183	78.7%
Uppercase Letter	4706	11.9%
Space Separator	3488	8.8%
Other Punctuation	121	0.3%
Dash Punctuation	106	0.3%
Open Punctuation	12	< 0.1%
Close Punctuation	12	< 0.1%
Connector Punctuation	6	< 0.1%
Decimal Number	5	< 0.1%
Final Punctuation	3	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
i	3804	12.2%
e	3547	11.4%
n	3147	10.1%
a	2281	7.3%
r	2272	7.3%
o	2269	7.3%
t	2226	7.1%
s	2024	6.5%
v	1429	4.6%
y	1358	4.4%
Other values (24)	6826	21.9%

Uppercase Letter

Value	Count	Frequency (%)
U	1183	25.1%
T	402	8.5%
I	365	7.8%
C	361	7.7%
M	353	7.5%
S	239	5.1%
A	205	4.4%
B	202	4.3%
D	192	4.1%
H	176	3.7%
Other values (17)	1028	21.8%

Other Punctuation

Value	Count	Frequency (%)
,	81	66.9%
&	23	19.0%
.	17	14.0%

Space Separator

Value	Count	Frequency (%)
	3488	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	106	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	12	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	12	100.0%

Connector Punctuation

Value	Count	Frequency (%)
_	6	100.0%

Decimal Number

Value	Count	Frequency (%)
3	5	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	3	100.0%

Other Number

Value	Count	Frequency (%)
²	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	35889	90.5%
Common	3754	9.5%

Most frequent character per script

Latin

Value	Count	Frequency (%)
i	3804	10.6%
e	3547	9.9%
n	3147	8.8%
a	2281	6.4%
r	2272	6.3%
o	2269	6.3%
t	2226	6.2%
s	2024	5.6%
v	1429	4.0%
y	1358	3.8%
Other values (51)	11532	32.1%

Common

Value	Count	Frequency (%)
	3488	92.9%
-	106	2.8%
,	81	2.2%
&	23	0.6%
.	17	0.5%
(	12	0.3%
)	12	0.3%
_	6	0.2%
3	5	0.1%
’	3	0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	39438	99.5%
None	202	0.5%
Punctuation	3	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
i	3804	9.6%
e	3547	9.0%
	3488	8.8%
n	3147	8.0%
a	2281	5.8%
r	2272	5.8%
o	2269	5.8%
t	2226	5.6%
s	2024	5.1%
v	1429	3.6%
Other values (50)	12951	32.8%

None

Value	Count	Frequency (%)
ó	62	30.7%
è	47	23.3%
é	39	19.3%
á	16	7.9%
à	11	5.4%
É	9	4.5%
ä	5	2.5%
ü	5	2.5%
ò	4	2.0%
ã	3	1.5%

Punctuation

Value	Count	Frequency (%)
’	3	100.0%

url
Text

Distinct	5269
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	41.3 KiB

Length

Max length	113
Median length	77
Mean length	59.307838
Min length	29

Characters and Unicode

Total characters	312493
Distinct characters	50
Distinct categories	6 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	5269 ?
Unique (%)	100.0%

Sample

1st row	https://www.coursera.org/learn/machine-learning
2nd row	https://www.coursera.org/learn/indigenous-canada
3rd row	https://www.coursera.org/learn/the-science-of-well-being
4th row	https://www.coursera.org/learn/technical-support-fundamentals
5th row	https://www.coursera.org/learn/google-cbrs-cpi-training

Value	Count	Frequency (%)
https://www.coursera.org/learn/machine-learning	1	< 0.1%
https://www.coursera.org/learn/food-and-health	1	< 0.1%
https://www.coursera.org/learn/technical-support-fundamentals	1	< 0.1%
https://www.coursera.org/learn/google-cbrs-cpi-training	1	< 0.1%
https://www.coursera.org/learn/financial-markets-global	1	< 0.1%
https://www.coursera.org/learn/introduction-psychology	1	< 0.1%
https://www.coursera.org/learn/python	1	< 0.1%
https://www.coursera.org/learn/computer-networking	1	< 0.1%
https://www.coursera.org/learn/ai-for-everyone	1	< 0.1%
https://www.coursera.org/learn/python-crash-course	1	< 0.1%
Other values (5259)	5259	99.8%

Most occurring characters

Value	Count	Frequency (%)
e	22929	7.3%
t	22919	7.3%
-	21952	7.0%
/	21076	6.7%
o	19222	6.2%
s	18055	5.8%
w	17671	5.7%
r	15701	5.0%
a	15104	4.8%
n	13470	4.3%
Other values (40)	124394	39.8%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	251994	80.6%
Other Punctuation	36883	11.8%
Dash Punctuation	21952	7.0%
Decimal Number	1614	0.5%
Connector Punctuation	34	< 0.1%
Uppercase Letter	16	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	22929	9.1%
t	22919	9.1%
o	19222	7.6%
s	18055	7.2%
w	17671	7.0%
r	15701	6.2%
a	15104	6.0%
n	13470	5.3%
i	12901	5.1%
c	12696	5.0%
Other values (16)	81326	32.3%

Decimal Number

Value	Count	Frequency (%)
1	419	26.0%
2	355	22.0%
0	250	15.5%
3	173	10.7%
5	167	10.3%
4	86	5.3%
6	57	3.5%
7	48	3.0%
8	34	2.1%
9	25	1.5%

Uppercase Letter

Value	Count	Frequency (%)
I	3	18.8%
M	3	18.8%
A	3	18.8%
C	2	12.5%
P	1	6.2%
D	1	6.2%
E	1	6.2%
S	1	6.2%
O	1	6.2%

Other Punctuation

Value	Count	Frequency (%)
/	21076	57.1%
.	10538	28.6%
:	5269	14.3%

Dash Punctuation

Value	Count	Frequency (%)
-	21952	100.0%

Connector Punctuation

Value	Count	Frequency (%)
_	34	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	252010	80.6%
Common	60483	19.4%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	22929	9.1%
t	22919	9.1%
o	19222	7.6%
s	18055	7.2%
w	17671	7.0%
r	15701	6.2%
a	15104	6.0%
n	13470	5.3%
i	12901	5.1%
c	12696	5.0%
Other values (25)	81342	32.3%

Common

Value	Count	Frequency (%)
-	21952	36.3%
/	21076	34.8%
.	10538	17.4%
:	5269	8.7%
1	419	0.7%
2	355	0.6%
0	250	0.4%
3	173	0.3%
5	167	0.3%
4	86	0.1%
Other values (5)	198	0.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	312493	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	22929	7.3%
t	22919	7.3%
-	21952	7.0%
/	21076	6.7%
o	19222	6.2%
s	18055	5.8%
w	17671	5.7%
r	15701	5.0%
a	15104	4.8%
n	13470	4.3%
Other values (40)	124394	39.8%

id
Text

Distinct	4295
Distinct (%)	100.0%
Missing	974
Missing (%)	18.5%
Memory size	41.3 KiB

Length

Max length	82
Median length	6
Mean length	8.563213
Min length	2

Characters and Unicode

Total characters	36779
Distinct characters	37
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	4295 ?
Unique (%)	100.0%

Sample

1st row	machine-learning
2nd row	indigenous-canada
3rd row	the-science-of-well-being
4th row	technical-support-fundamentals
5th row	google-cbrs-cpi-training

Value	Count	Frequency (%)
machine-learning	1	< 0.1%
uva-darden-project-management	1	< 0.1%
os-power-user	1	< 0.1%
the-science-of-well-being	1	< 0.1%
technical-support-fundamentals	1	< 0.1%
google-cbrs-cpi-training	1	< 0.1%
financial-markets-global	1	< 0.1%
introduction-psychology	1	< 0.1%
python	1	< 0.1%
computer-networking	1	< 0.1%
Other values (4285)	4285	99.8%

Most occurring characters

Value	Count	Frequency (%)
1	2867	7.8%
6	2634	7.2%
2	2593	7.1%
4	2526	6.9%
8	2418	6.6%
0	2396	6.5%
5	1833	5.0%
9	1821	5.0%
7	1815	4.9%
3	1736	4.7%
Other values (27)	14140	38.4%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	22639	61.6%
Lowercase Letter	12982	35.3%
Dash Punctuation	1158	3.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	1247	9.6%
n	1214	9.4%
i	1208	9.3%
a	1197	9.2%
t	1019	7.8%
o	925	7.1%
s	842	6.5%
r	794	6.1%
c	710	5.5%
l	598	4.6%
Other values (16)	3228	24.9%

Decimal Number

Value	Count	Frequency (%)
1	2867	12.7%
6	2634	11.6%
2	2593	11.5%
4	2526	11.2%
8	2418	10.7%
0	2396	10.6%
5	1833	8.1%
9	1821	8.0%
7	1815	8.0%
3	1736	7.7%

Dash Punctuation

Value	Count	Frequency (%)
-	1158	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	23797	64.7%
Latin	12982	35.3%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	1247	9.6%
n	1214	9.4%
i	1208	9.3%
a	1197	9.2%
t	1019	7.8%
o	925	7.1%
s	842	6.5%
r	794	6.1%
c	710	5.5%
l	598	4.6%
Other values (16)	3228	24.9%

Common

Value	Count	Frequency (%)
1	2867	12.0%
6	2634	11.1%
2	2593	10.9%
4	2526	10.6%
8	2418	10.2%
0	2396	10.1%
5	1833	7.7%
9	1821	7.7%
7	1815	7.6%
3	1736	7.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	36779	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
1	2867	7.8%
6	2634	7.2%
2	2593	7.1%
4	2526	6.9%
8	2418	6.6%
0	2396	6.5%
5	1833	5.0%
9	1821	5.0%
7	1815	4.9%
3	1736	4.7%
Other values (27)	14140	38.4%

mooc
Categorical

Distinct	3
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	41.3 KiB

udemy	3672
edx	974
coursera	623

Length

Max length	8
Median length	5
Mean length	4.9850066
Min length	3

Characters and Unicode

Total characters	26266
Distinct characters	11
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	coursera
2nd row	coursera
3rd row	coursera
4th row	coursera
5th row	coursera

Common Values

Value	Count	Frequency (%)
udemy	3672	69.7%
edx	974	18.5%
coursera	623	11.8%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
udemy	3672	69.7%
edx	974	18.5%
coursera	623	11.8%

Most occurring characters

Value	Count	Frequency (%)
e	5269	20.1%
d	4646	17.7%
u	4295	16.4%
m	3672	14.0%
y	3672	14.0%
r	1246	4.7%
x	974	3.7%
c	623	2.4%
o	623	2.4%
s	623	2.4%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	26266	100.0%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	5269	20.1%
d	4646	17.7%
u	4295	16.4%
m	3672	14.0%
y	3672	14.0%
r	1246	4.7%
x	974	3.7%
c	623	2.4%
o	623	2.4%
s	623	2.4%

Most occurring scripts

Value	Count	Frequency (%)
Latin	26266	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	5269	20.1%
d	4646	17.7%
u	4295	16.4%
m	3672	14.0%
y	3672	14.0%
r	1246	4.7%
x	974	3.7%
c	623	2.4%
o	623	2.4%
s	623	2.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	26266	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	5269	20.1%
d	4646	17.7%
u	4295	16.4%
m	3672	14.0%
y	3672	14.0%
r	1246	4.7%
x	974	3.7%
c	623	2.4%
o	623	2.4%
s	623	2.4%

summary
Text

Distinct	887
Distinct (%)	96.3%
Missing	4348
Missing (%)	82.5%
Memory size	41.3 KiB

Length

Max length	783
Median length	245
Mean length	150.54723
Min length	39

Characters and Unicode

Total characters	138654
Distinct characters	385
Distinct categories	15 ?
Distinct scripts	4 ?
Distinct blocks	5 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	883 ?
Unique (%)	95.9%

Sample

1st row	Learn essential strategies for successful online learning
2nd row	This course is a "no prerequisite" introduction to Python Programming. You will learn about variables, conditional execution, repeated execution and how we use functions. The homework is done in a web browser so you can do all of the programming assignments on a phone or public computer.
3rd row	An introduction to the intellectual enterprises of computer science and the art of programming.
4th row	Through inspiring examples and stories, discover the power of data and use analytics to provide an edge to your career and your life.
5th row	This course is part of a MicroMasters® Program

Value	Count	Frequency (%)
and	1055	5.1%
the	764	3.7%
to	613	2.9%
of	545	2.6%
learn	395	1.9%
a	352	1.7%
de	348	1.7%
in	303	1.5%
how	276	1.3%
y	224	1.1%
Other values (4749)	15978	76.6%

Most occurring characters

Value	Count	Frequency (%)
	19939	14.4%
e	13093	9.4%
a	10335	7.5%
n	9176	6.6%
o	8890	6.4%
i	8709	6.3%
t	8382	6.0%
s	7847	5.7%
r	7649	5.5%
l	5049	3.6%
Other values (375)	39585	28.5%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	112333	81.0%
Space Separator	19939	14.4%
Uppercase Letter	2823	2.0%
Other Punctuation	2321	1.7%
Other Letter	703	0.5%
Dash Punctuation	215	0.2%
Decimal Number	134	0.1%
Final Punctuation	58	< 0.1%
Open Punctuation	43	< 0.1%
Close Punctuation	42	< 0.1%
Other values (5)	43	< 0.1%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
ل	34	4.8%
ا	34	4.8%
的	26	3.7%
م	20	2.8%
و	17	2.4%
ت	17	2.4%
ي	16	2.3%
学	15	2.1%
ة	12	1.7%
ع	10	1.4%
Other values (261)	502	71.4%

Lowercase Letter

Value	Count	Frequency (%)
e	13093	11.7%
a	10335	9.2%
n	9176	8.2%
o	8890	7.9%
i	8709	7.8%
t	8382	7.5%
s	7847	7.0%
r	7649	6.8%
l	5049	4.5%
c	5001	4.5%
Other values (32)	28202	25.1%

Uppercase Letter

Value	Count	Frequency (%)
L	440	15.6%
A	270	9.6%
T	270	9.6%
E	205	7.3%
C	197	7.0%
I	177	6.3%
P	171	6.1%
M	170	6.0%
S	163	5.8%
D	152	5.4%
Other values (16)	608	21.5%

Other Punctuation

Value	Count	Frequency (%)
.	1067	46.0%
,	1023	44.1%
'	45	1.9%
?	40	1.7%
!	30	1.3%
"	26	1.1%
:	24	1.0%
，	15	0.6%
、	11	0.5%
。	9	0.4%
Other values (10)	31	1.3%

Decimal Number

Value	Count	Frequency (%)
0	37	27.6%
1	31	23.1%
2	18	13.4%
5	15	11.2%
4	10	7.5%
3	9	6.7%
8	5	3.7%
9	5	3.7%
7	3	2.2%
6	1	0.7%

Dash Punctuation

Value	Count	Frequency (%)
-	204	94.9%
–	7	3.3%
—	4	1.9%

Final Punctuation

Value	Count	Frequency (%)
’	50	86.2%
”	7	12.1%
»	1	1.7%

Initial Punctuation

Value	Count	Frequency (%)
“	7	87.5%
«	1	12.5%

Math Symbol

Value	Count	Frequency (%)
+	4	80.0%
\|	1	20.0%

Space Separator

Value	Count	Frequency (%)
	19939	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	43	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	42	100.0%

Other Symbol

Value	Count	Frequency (%)
®	25	100.0%

Format

Value	Count	Frequency (%)
	3	100.0%

Control

Value	Count	Frequency (%)
	2	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	115156	83.1%
Common	22795	16.4%
Han	466	0.3%
Arabic	237	0.2%

Most frequent character per script

Han

Value	Count	Frequency (%)
的	26	5.6%
学	15	3.2%
和	10	2.1%
方	8	1.7%
本	7	1.5%
理	7	1.5%
科	7	1.5%
一	6	1.3%
与	6	1.3%
以	5	1.1%
Other values (231)	369	79.2%

Latin

Value	Count	Frequency (%)
e	13093	11.4%
a	10335	9.0%
n	9176	8.0%
o	8890	7.7%
i	8709	7.6%
t	8382	7.3%
s	7847	6.8%
r	7649	6.6%
l	5049	4.4%
c	5001	4.3%
Other values (58)	31025	26.9%

Common

Value	Count	Frequency (%)
	19939	87.5%
.	1067	4.7%
,	1023	4.5%
-	204	0.9%
’	50	0.2%
'	45	0.2%
(	43	0.2%
)	42	0.2%
?	40	0.2%
0	37	0.2%
Other values (36)	305	1.3%

Arabic

Value	Count	Frequency (%)
ل	34	14.3%
ا	34	14.3%
م	20	8.4%
و	17	7.2%
ت	17	7.2%
ي	16	6.8%
ة	12	5.1%
ع	10	4.2%
ف	8	3.4%
ب	6	2.5%
Other values (20)	63	26.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	137243	99.0%
None	626	0.5%
CJK	466	0.3%
Arabic	241	0.2%
Punctuation	78	0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	19939	14.5%
e	13093	9.5%
a	10335	7.5%
n	9176	6.7%
o	8890	6.5%
i	8709	6.3%
t	8382	6.1%
s	7847	5.7%
r	7649	5.6%
l	5049	3.7%
Other values (71)	38174	27.8%

None

Value	Count	Frequency (%)
ó	184	29.4%
á	146	23.3%
í	84	13.4%
é	80	12.8%
®	25	4.0%
ñ	20	3.2%
ú	18	2.9%
，	15	2.4%
、	11	1.8%
。	9	1.4%
Other values (16)	34	5.4%

Punctuation

Value	Count	Frequency (%)
’	50	64.1%
“	7	9.0%
–	7	9.0%
”	7	9.0%
—	4	5.1%
	3	3.8%

Arabic

Value	Count	Frequency (%)
ل	34	14.1%
ا	34	14.1%
م	20	8.3%
و	17	7.1%
ت	17	7.1%
ي	16	6.6%
ة	12	5.0%
ع	10	4.1%
ف	8	3.3%
ب	6	2.5%
Other values (21)	67	27.8%

CJK

Value	Count	Frequency (%)
的	26	5.6%
学	15	3.2%
和	10	2.1%
方	8	1.7%
本	7	1.5%
理	7	1.5%
科	7	1.5%
一	6	1.3%
与	6	1.3%
以	5	1.1%
Other values (231)	369	79.2%

n_subscribers
Real number (ℝ)

HIGH CORRELATION MISSING SKEWED ZEROS

Distinct	3033
Distinct (%)	67.0%
Missing	743
Missing (%)	14.1%
Infinite	0
Infinite (%)	0.0%
Mean	12628.401

Minimum	0
Maximum	2442271
Zeros	65
Zeros (%)	1.2%
Negative	0
Negative (%)	0.0%
Memory size	41.3 KiB

Quantile statistics

Minimum	0
5-th percentile	6
Q1	214
median	1423.5
Q3	7303.75
95-th percentile	55996.25
Maximum	2442271
Range	2442271
Interquartile range (IQR)	7089.75

Descriptive statistics

Standard deviation	55943.377
Coefficient of variation (CV)	4.4299651
Kurtosis	857.41469
Mean	12628.401
Median Absolute Deviation (MAD)	1389.5
Skewness	23.310188
Sum	57156144
Variance	3.1296614 × 10⁹
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	65	1.2%
1	49	0.9%
5	28	0.5%
2	27	0.5%
3	26	0.5%
4	26	0.5%
7	24	0.5%
11	23	0.4%
13	19	0.4%
9	18	0.3%
Other values (3023)	4221	80.1%
(Missing)	743	14.1%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	65	1.2%
1	49	0.9%
2	27	0.5%
3	26	0.5%
4	26	0.5%
5	28	0.5%
6	18	0.3%
7	24	0.5%
8	18	0.3%
9	18	0.3%

Value	Count	Frequency (%)
2442271	1	< 0.1%
1103777	1	< 0.1%
1022489	1	< 0.1%
698950	1	< 0.1%
642088	1	< 0.1%
528782	1	< 0.1%
475614	1	< 0.1%
414181	1	< 0.1%
406181	1	< 0.1%
400169	1	< 0.1%

modality
Categorical

HIGH CORRELATION IMBALANCE MISSING

Distinct	2
Distinct (%)	0.2%
Missing	4295
Missing (%)	81.5%
Memory size	41.3 KiB

Self-paced on your time	916
Instructor-led on a course schedule	58

Length

Max length	35
Median length	23
Mean length	23.714579
Min length	23

Characters and Unicode

Total characters	23098
Distinct characters	21
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Self-paced on your time
2nd row	Self-paced on your time
3rd row	Self-paced on your time
4th row	Instructor-led on a course schedule
5th row	Self-paced on your time

Common Values

Value	Count	Frequency (%)
Self-paced on your time	916	17.4%
Instructor-led on a course schedule	58	1.1%
(Missing)	4295	81.5%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
on	974	24.6%
self-paced	916	23.2%
your	916	23.2%
time	916	23.2%
instructor-led	58	1.5%
a	58	1.5%
course	58	1.5%
schedule	58	1.5%

Most occurring characters

Value	Count	Frequency (%)
	2980	12.9%
e	2980	12.9%
o	2006	8.7%
r	1090	4.7%
c	1090	4.7%
u	1090	4.7%
l	1032	4.5%
d	1032	4.5%
n	1032	4.5%
t	1032	4.5%
Other values (11)	7734	33.5%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	18170	78.7%
Space Separator	2980	12.9%
Dash Punctuation	974	4.2%
Uppercase Letter	974	4.2%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	2980	16.4%
o	2006	11.0%
r	1090	6.0%
c	1090	6.0%
u	1090	6.0%
l	1032	5.7%
d	1032	5.7%
n	1032	5.7%
t	1032	5.7%
a	974	5.4%
Other values (7)	4812	26.5%

Uppercase Letter

Value	Count	Frequency (%)
S	916	94.0%
I	58	6.0%

Space Separator

Value	Count	Frequency (%)
	2980	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	974	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	19144	82.9%
Common	3954	17.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	2980	15.6%
o	2006	10.5%
r	1090	5.7%
c	1090	5.7%
u	1090	5.7%
l	1032	5.4%
d	1032	5.4%
n	1032	5.4%
t	1032	5.4%
a	974	5.1%
Other values (9)	5786	30.2%

Common

Value	Count	Frequency (%)
	2980	75.4%
-	974	24.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	23098	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	2980	12.9%
e	2980	12.9%
o	2006	8.7%
r	1090	4.7%
c	1090	4.7%
u	1090	4.7%
l	1032	4.5%
d	1032	4.5%
n	1032	4.5%
t	1032	4.5%
Other values (11)	7734	33.5%

instructors
Text

Distinct	775
Distinct (%)	79.8%
Missing	4298
Missing (%)	81.6%
Memory size	41.3 KiB

Length

Max length	121
Median length	85
Mean length	33.741504
Min length	9

Characters and Unicode

Total characters	32763
Distinct characters	92
Distinct categories	10 ?
Distinct scripts	3 ?
Distinct blocks	4 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	675 ?
Unique (%)	69.5%

Sample

1st row	Nina Huntemann-Robyn Belair-Ben Piscopo
2nd row	Charles Severance
3rd row	David J. Malan-Doug Lloyd-Brian Yu
4th row	Dimitris Bertsimas-Allison O'Hair-John Silberholz-Iain Dunning
5th row	Stephan Sorger

Value	Count	Frequency (%)
david	38	1.0%
van	29	0.8%
de	27	0.7%
peter	20	0.5%
j	19	0.5%
rafael	18	0.5%
m	16	0.4%
d	14	0.4%
thomas	14	0.4%
dr	14	0.4%
Other values (2371)	3566	94.5%

Most occurring characters

Value	Count	Frequency (%)
a	3248	9.9%
	2808	8.6%
e	2738	8.4%
n	2146	6.6%
r	2146	6.6%
i	1925	5.9%
o	1713	5.2%
l	1391	4.2%
s	1167	3.6%
-	1131	3.5%
Other values (82)	12350	37.7%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	23564	71.9%
Uppercase Letter	4981	15.2%
Space Separator	2808	8.6%
Dash Punctuation	1132	3.5%
Other Punctuation	241	0.7%
Other Letter	14	< 0.1%
Open Punctuation	8	< 0.1%
Close Punctuation	8	< 0.1%
Format	5	< 0.1%
Final Punctuation	2	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
a	3248	13.8%
e	2738	11.6%
n	2146	9.1%
r	2146	9.1%
i	1925	8.2%
o	1713	7.3%
l	1391	5.9%
s	1167	5.0%
t	986	4.2%
h	732	3.1%
Other values (30)	5372	22.8%

Uppercase Letter

Value	Count	Frequency (%)
M	521	10.5%
S	402	8.1%
A	387	7.8%
C	324	6.5%
J	312	6.3%
D	311	6.2%
P	309	6.2%
B	285	5.7%
R	282	5.7%
G	245	4.9%
Other values (20)	1603	32.2%

Other Letter

Value	Count	Frequency (%)
ª	2	14.3%
س	2	14.3%
و	2	14.3%
ل	2	14.3%
ج	1	7.1%
ى	1	7.1%
م	1	7.1%
ة	1	7.1%
ا	1	7.1%
ب	1	7.1%

Other Punctuation

Value	Count	Frequency (%)
.	210	87.1%
,	24	10.0%
'	3	1.2%
:	2	0.8%
"	2	0.8%

Dash Punctuation

Value	Count	Frequency (%)
-	1131	99.9%
–	1	0.1%

Space Separator

Value	Count	Frequency (%)
	2808	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	8	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	8	100.0%

Format

Value	Count	Frequency (%)
	5	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	2	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	28547	87.1%
Common	4204	12.8%
Arabic	12	< 0.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
a	3248	11.4%
e	2738	9.6%
n	2146	7.5%
r	2146	7.5%
i	1925	6.7%
o	1713	6.0%
l	1391	4.9%
s	1167	4.1%
t	986	3.5%
h	732	2.6%
Other values (61)	10355	36.3%

Common

Value	Count	Frequency (%)
	2808	66.8%
-	1131	26.9%
.	210	5.0%
,	24	0.6%
(	8	0.2%
)	8	0.2%
	5	0.1%
'	3	0.1%
:	2	< 0.1%
"	2	< 0.1%
Other values (2)	3	0.1%

Arabic

Value	Count	Frequency (%)
س	2	16.7%
و	2	16.7%
ل	2	16.7%
ج	1	8.3%
ى	1	8.3%
م	1	8.3%
ة	1	8.3%
ا	1	8.3%
ب	1	8.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	32418	98.9%
None	325	1.0%
Arabic	12	< 0.1%
Punctuation	8	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
a	3248	10.0%
	2808	8.7%
e	2738	8.4%
n	2146	6.6%
r	2146	6.6%
i	1925	5.9%
o	1713	5.3%
l	1391	4.3%
s	1167	3.6%
-	1131	3.5%
Other values (51)	12005	37.0%

None

Value	Count	Frequency (%)
í	91	28.0%
á	70	21.5%
é	66	20.3%
ó	36	11.1%
ñ	18	5.5%
Á	16	4.9%
ú	10	3.1%
ö	3	0.9%
ü	2	0.6%
ä	2	0.6%
Other values (9)	11	3.4%

Punctuation

Value	Count	Frequency (%)
	5	62.5%
’	2	25.0%
–	1	12.5%

Arabic

Value	Count	Frequency (%)
س	2	16.7%
و	2	16.7%
ل	2	16.7%
ج	1	8.3%
ى	1	8.3%
م	1	8.3%
ة	1	8.3%
ا	1	8.3%
ب	1	8.3%

level
Categorical

HIGH CORRELATION MISSING

Distinct	7
Distinct (%)	0.2%
Missing	623
Missing (%)	11.8%
Memory size	41.3 KiB

All Levels	1925
Beginner Level	1268
Introductory	621
Intermediate Level	421
Intermediate	266
Other values (2)	145

Length

Max length	18
Median length	14
Mean length	12.185966
Min length	8

Characters and Unicode

Total characters	56616
Distinct characters	24
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Introductory
2nd row	Introductory
3rd row	Introductory
4th row	Intermediate
5th row	Introductory

Common Values

Value	Count	Frequency (%)
All Levels	1925	36.5%
Beginner Level	1268	24.1%
Introductory	621	11.8%
Intermediate Level	421	8.0%
Intermediate	266	5.0%
Advanced	87	1.7%
Expert Level	58	1.1%
(Missing)	623	11.8%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
all	1925	23.1%
levels	1925	23.1%
level	1747	21.0%
beginner	1268	15.2%
intermediate	687	8.3%
introductory	621	7.5%
advanced	87	1.0%
expert	58	0.7%

Most occurring characters

Value	Count	Frequency (%)
e	12086	21.3%
l	7522	13.3%
n	3931	6.9%
v	3759	6.6%
	3672	6.5%
L	3672	6.5%
r	3255	5.7%
t	2674	4.7%
A	2012	3.6%
i	1955	3.5%
Other values (14)	12078	21.3%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	44626	78.8%
Uppercase Letter	8318	14.7%
Space Separator	3672	6.5%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	12086	27.1%
l	7522	16.9%
n	3931	8.8%
v	3759	8.4%
r	3255	7.3%
t	2674	6.0%
i	1955	4.4%
s	1925	4.3%
d	1482	3.3%
g	1268	2.8%
Other values (8)	4769	10.7%

Uppercase Letter

Value	Count	Frequency (%)
L	3672	44.1%
A	2012	24.2%
I	1308	15.7%
B	1268	15.2%
E	58	0.7%

Space Separator

Value	Count	Frequency (%)
	3672	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	52944	93.5%
Common	3672	6.5%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	12086	22.8%
l	7522	14.2%
n	3931	7.4%
v	3759	7.1%
L	3672	6.9%
r	3255	6.1%
t	2674	5.1%
A	2012	3.8%
i	1955	3.7%
s	1925	3.6%
Other values (13)	10153	19.2%

Common

Value	Count	Frequency (%)
	3672	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	56616	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	12086	21.3%
l	7522	13.3%
n	3931	6.9%
v	3759	6.6%
	3672	6.5%
L	3672	6.5%
r	3255	5.7%
t	2674	4.7%
A	2012	3.6%
i	1955	3.5%
Other values (14)	12078	21.3%

subject
Categorical

HIGH CORRELATION MISSING

Distinct	35
Distinct (%)	0.8%
Missing	623
Missing (%)	11.8%
Memory size	41.3 KiB

Web Development	1199
Business Finance	1191
Musical Instruments	680
Graphic Design	602
Computer Science	166
Other values (30)	808

Length

Max length	28
Median length	26
Mean length	15.876022
Min length	3

Characters and Unicode

Total characters	73760
Distinct characters	39
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	< 0.1%

Sample

1st row	Education & Teacher Training
2nd row	Computer Science
3rd row	Computer Science
4th row	Data Analysis & Statistics
5th row	Computer Science

Common Values

Value	Count	Frequency (%)
Web Development	1199	22.8%
Business Finance	1191	22.6%
Musical Instruments	680	12.9%
Graphic Design	602	11.4%
Computer Science	166	3.2%
Business & Management	164	3.1%
Data Analysis & Statistics	71	1.3%
Humanities	64	1.2%
Engineering	58	1.1%
Social Sciences	51	1.0%
Other values (25)	400	7.6%
(Missing)	623	11.8%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
business	1355	14.3%
finance	1237	13.1%
web	1199	12.7%
development	1199	12.7%
musical	680	7.2%
instruments	680	7.2%
design	610	6.4%
graphic	602	6.4%
	388	4.1%
science	176	1.9%
Other values (40)	1344	14.2%

Most occurring characters

Value	Count	Frequency (%)
e	10185	13.8%
n	8251	11.2%
s	7307	9.9%
i	5729	7.8%
	4824	6.5%
a	3532	4.8%
t	3492	4.7%
c	3457	4.7%
u	3109	4.2%
m	2418	3.3%
Other values (29)	21456	29.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	59466	80.6%
Uppercase Letter	9082	12.3%
Space Separator	4824	6.5%
Other Punctuation	388	0.5%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	10185	17.1%
n	8251	13.9%
s	7307	12.3%
i	5729	9.6%
a	3532	5.9%
t	3492	5.9%
c	3457	5.8%
u	3109	5.2%
m	2418	4.1%
l	2089	3.5%
Other values (11)	9897	16.6%

Uppercase Letter

Value	Count	Frequency (%)
D	1880	20.7%
B	1390	15.3%
F	1243	13.7%
W	1199	13.2%
M	913	10.1%
I	680	7.5%
G	602	6.6%
S	419	4.6%
C	222	2.4%
E	179	2.0%
Other values (6)	355	3.9%

Space Separator

Value	Count	Frequency (%)
	4824	100.0%

Other Punctuation

Value	Count	Frequency (%)
&	388	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	68548	92.9%
Common	5212	7.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	10185	14.9%
n	8251	12.0%
s	7307	10.7%
i	5729	8.4%
a	3532	5.2%
t	3492	5.1%
c	3457	5.0%
u	3109	4.5%
m	2418	3.5%
l	2089	3.0%
Other values (27)	18979	27.7%

Common

Value	Count	Frequency (%)
	4824	92.6%
&	388	7.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	73760	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	10185	13.8%
n	8251	11.2%
s	7307	9.9%
i	5729	7.8%
	4824	6.5%
a	3532	4.8%
t	3492	4.7%
c	3457	4.7%
u	3109	4.2%
m	2418	3.3%
Other values (29)	21456	29.1%

language
Categorical

HIGH CORRELATION IMBALANCE MISSING

Distinct	9
Distinct (%)	0.9%
Missing	4295
Missing (%)	81.5%
Memory size	41.3 KiB

English	776
Español	176
Français	7
Italiano	4
中文	4
Other values (4)	7

Length

Max length	13
Median length	7
Mean length	7.0010267
Min length	2

Characters and Unicode

Total characters	6819
Distinct characters	36
Distinct categories	4 ?
Distinct scripts	4 ?
Distinct blocks	4 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3 ?
Unique (%)	0.3%

Sample

1st row	English
2nd row	English
3rd row	English
4th row	English
5th row	English

Common Values

Value	Count	Frequency (%)
English	776	14.7%
Español	176	3.3%
Français	7	0.1%
Italiano	4	0.1%
中文	4	0.1%
Português	4	0.1%
日本語	1	< 0.1%
اللغة العربية	1	< 0.1%
Deutsch	1	< 0.1%
(Missing)	4295	81.5%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
english	776	79.6%
español	176	18.1%
français	7	0.7%
italiano	4	0.4%
中文	4	0.4%
português	4	0.4%
日本語	1	0.1%
اللغة	1	0.1%
العربية	1	0.1%
deutsch	1	0.1%

Most occurring characters

Value	Count	Frequency (%)
s	964	14.1%
l	956	14.0%
E	952	14.0%
n	787	11.5%
i	787	11.5%
g	780	11.4%
h	777	11.4%
a	198	2.9%
o	184	2.7%
p	176	2.6%
Other values (26)	258	3.8%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	5827	85.5%
Uppercase Letter	968	14.2%
Other Letter	23	0.3%
Space Separator	1	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
s	964	16.5%
l	956	16.4%
n	787	13.5%
i	787	13.5%
g	780	13.4%
h	777	13.3%
a	198	3.4%
o	184	3.2%
p	176	3.0%
ñ	176	3.0%
Other values (7)	42	0.7%

Other Letter

Value	Count	Frequency (%)
文	4	17.4%
中	4	17.4%
ل	3	13.0%
ا	2	8.7%
ة	2	8.7%
語	1	4.3%
本	1	4.3%
غ	1	4.3%
日	1	4.3%
ع	1	4.3%
Other values (3)	3	13.0%

Uppercase Letter

Value	Count	Frequency (%)
E	952	98.3%
F	7	0.7%
P	4	0.4%
I	4	0.4%
D	1	0.1%

Space Separator

Value	Count	Frequency (%)
	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	6795	99.6%
Arabic	12	0.2%
Han	11	0.2%
Common	1	< 0.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
s	964	14.2%
l	956	14.1%
E	952	14.0%
n	787	11.6%
i	787	11.6%
g	780	11.5%
h	777	11.4%
a	198	2.9%
o	184	2.7%
p	176	2.6%
Other values (12)	234	3.4%

Arabic

Value	Count	Frequency (%)
ل	3	25.0%
ا	2	16.7%
ة	2	16.7%
غ	1	8.3%
ع	1	8.3%
ر	1	8.3%
ب	1	8.3%
ي	1	8.3%

Han

Value	Count	Frequency (%)
文	4	36.4%
中	4	36.4%
語	1	9.1%
本	1	9.1%
日	1	9.1%

Common

Value	Count	Frequency (%)
	1	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	6609	96.9%
None	187	2.7%
Arabic	12	0.2%
CJK	11	0.2%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
s	964	14.6%
l	956	14.5%
E	952	14.4%
n	787	11.9%
i	787	11.9%
g	780	11.8%
h	777	11.8%
a	198	3.0%
o	184	2.8%
p	176	2.7%
Other values (10)	48	0.7%

None

Value	Count	Frequency (%)
ñ	176	94.1%
ç	7	3.7%
ê	4	2.1%

CJK

Value	Count	Frequency (%)
文	4	36.4%
中	4	36.4%
語	1	9.1%
本	1	9.1%
日	1	9.1%

Arabic

Value	Count	Frequency (%)
ل	3	25.0%
ا	2	16.7%
ة	2	16.7%
غ	1	8.3%
ع	1	8.3%
ر	1	8.3%
ب	1	8.3%
ي	1	8.3%

subtitles
Categorical

HIGH CORRELATION IMBALANCE MISSING

Distinct	33
Distinct (%)	3.4%
Missing	4298
Missing (%)	81.6%
Memory size	41.3 KiB

English	712
Español	157
English, 中文	21
English, Español	21
English, हिन्दी	10
Other values (28)	50

Length

Max length	79
Median length	7
Mean length	8.015448
Min length	7

Characters and Unicode

Total characters	7783
Distinct characters	61
Distinct categories	7 ?
Distinct scripts	8 ?
Distinct blocks	8 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	20 ?
Unique (%)	2.1%

Sample

1st row	English
2nd row	English
3rd row	English
4th row	English
5th row	English

Common Values

Value	Count	Frequency (%)
English	712	13.5%
Español	157	3.0%
English, 中文	21	0.4%
English, Español	21	0.4%
English, हिन्दी	10	0.2%
Français	7	0.1%
English, Русский	5	0.1%
Italiano	4	0.1%
Português	4	0.1%
English, 日本語	3	0.1%
Other values (23)	27	0.5%
(Missing)	4298	81.6%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
english	796	72.2%
español	190	17.2%
中文	37	3.4%
français	16	1.5%
हिन्दी	11	1.0%
português	11	1.0%
русский	9	0.8%
italiano	6	0.5%
日本語	6	0.5%
اللغة	6	0.5%
Other values (6)	15	1.4%

Most occurring characters

Value	Count	Frequency (%)
s	1017	13.1%
l	992	12.7%
E	986	12.7%
n	821	10.5%
i	819	10.5%
g	807	10.4%
h	799	10.3%
a	235	3.0%
o	208	2.7%
ñ	190	2.4%
Other values (51)	909	11.7%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	6244	80.2%
Uppercase Letter	1033	13.3%
Other Letter	215	2.8%
Space Separator	132	1.7%
Other Punctuation	126	1.6%
Spacing Mark	22	0.3%
Nonspacing Mark	11	0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
s	1017	16.3%
l	992	15.9%
n	821	13.1%
i	819	13.1%
g	807	12.9%
h	799	12.8%
a	235	3.8%
o	208	3.3%
ñ	190	3.0%
p	190	3.0%
Other values (15)	166	2.7%

Other Letter

Value	Count	Frequency (%)
中	37	17.2%
文	37	17.2%
ل	18	8.4%
ة	12	5.6%
ا	12	5.6%
न	11	5.1%
ह	11	5.1%
द	11	5.1%
語	6	2.8%
غ	6	2.8%
Other values (14)	54	25.1%

Uppercase Letter

Value	Count	Frequency (%)
E	986	95.5%
F	16	1.5%
P	11	1.1%
Р	9	0.9%
I	7	0.7%
D	3	0.3%
T	1	0.1%

Spacing Mark

Value	Count	Frequency (%)
ी	11	50.0%
ि	11	50.0%

Space Separator

Value	Count	Frequency (%)
	132	100.0%

Other Punctuation

Value	Count	Frequency (%)
,	126	100.0%

Nonspacing Mark

Value	Count	Frequency (%)
्	11	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	7214	92.7%
Common	258	3.3%
Han	92	1.2%
Arabic	72	0.9%
Devanagari	66	0.8%
Cyrillic	63	0.8%
Hebrew	15	0.2%
Hangul	3	< 0.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
s	1017	14.1%
l	992	13.8%
E	986	13.7%
n	821	11.4%
i	819	11.4%
g	807	11.2%
h	799	11.1%
a	235	3.3%
o	208	2.9%
ñ	190	2.6%
Other values (16)	340	4.7%

Arabic

Value	Count	Frequency (%)
ل	18	25.0%
ة	12	16.7%
ا	12	16.7%
غ	6	8.3%
ع	6	8.3%
ر	6	8.3%
ب	6	8.3%
ي	6	8.3%

Cyrillic

Value	Count	Frequency (%)
с	18	28.6%
й	9	14.3%
к	9	14.3%
у	9	14.3%
Р	9	14.3%
и	9	14.3%

Devanagari

Value	Count	Frequency (%)
ी	11	16.7%
न	11	16.7%
ह	11	16.7%
ि	11	16.7%
्	11	16.7%
द	11	16.7%

Han

Value	Count	Frequency (%)
中	37	40.2%
文	37	40.2%
語	6	6.5%
日	6	6.5%
本	6	6.5%

Hebrew

Value	Count	Frequency (%)
ת	3	20.0%
י	3	20.0%
ר	3	20.0%
ב	3	20.0%
ע	3	20.0%

Hangul

Value	Count	Frequency (%)
한	1	33.3%
국	1	33.3%
어	1	33.3%

Common

Value	Count	Frequency (%)
	132	51.2%
,	126	48.8%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	7253	93.2%
None	219	2.8%
CJK	92	1.2%
Arabic	72	0.9%
Devanagari	66	0.8%
Cyrillic	63	0.8%
Hebrew	15	0.2%
Hangul	3	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
s	1017	14.0%
l	992	13.7%
E	986	13.6%
n	821	11.3%
i	819	11.3%
g	807	11.1%
h	799	11.0%
a	235	3.2%
o	208	2.9%
p	190	2.6%
Other values (14)	379	5.2%

None

Value	Count	Frequency (%)
ñ	190	86.8%
ç	17	7.8%
ê	11	5.0%
ü	1	0.5%

CJK

Value	Count	Frequency (%)
中	37	40.2%
文	37	40.2%
語	6	6.5%
日	6	6.5%
本	6	6.5%

Cyrillic

Value	Count	Frequency (%)
с	18	28.6%
й	9	14.3%
к	9	14.3%
у	9	14.3%
Р	9	14.3%
и	9	14.3%

Arabic

Value	Count	Frequency (%)
ل	18	25.0%
ة	12	16.7%
ا	12	16.7%
غ	6	8.3%
ع	6	8.3%
ر	6	8.3%
ب	6	8.3%
ي	6	8.3%

Devanagari

Value	Count	Frequency (%)
ी	11	16.7%
न	11	16.7%
ह	11	16.7%
ि	11	16.7%
्	11	16.7%
द	11	16.7%

Hebrew

Value	Count	Frequency (%)
ת	3	20.0%
י	3	20.0%
ר	3	20.0%
ב	3	20.0%
ע	3	20.0%

Hangul

Value	Count	Frequency (%)
한	1	33.3%
국	1	33.3%
어	1	33.3%

effort
Text

Distinct	53
Distinct (%)	5.4%
Missing	4295
Missing (%)	81.5%
Memory size	41.3 KiB

Length

Max length	20
Median length	18
Mean length	18.205339
Min length	18

Characters and Unicode

Total characters	17732
Distinct characters	21
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	17 ?
Unique (%)	1.7%

Sample

1st row	2–3 hours per week
2nd row	2–4 hours per week
3rd row	6–18 hours per week
4th row	10–15 hours per week
5th row	5–7 hours per week

Value	Count	Frequency (%)
hours	974	25.0%
per	974	25.0%
week	974	25.0%
2–4	108	2.8%
2–3	104	2.7%
3–5	103	2.6%
3–4	91	2.3%
4–6	79	2.0%
8–10	57	1.5%
1–2	55	1.4%
Other values (46)	377	9.7%

Most occurring characters

Value	Count	Frequency (%)
e	2922	16.5%
	2922	16.5%
r	1948	11.0%
h	974	5.5%
o	974	5.5%
u	974	5.5%
s	974	5.5%
p	974	5.5%
–	974	5.5%
w	974	5.5%
Other values (11)	3122	17.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	11688	65.9%
Space Separator	2922	16.5%
Decimal Number	2148	12.1%
Dash Punctuation	974	5.5%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
4	357	16.6%
3	351	16.3%
2	331	15.4%
1	282	13.1%
5	275	12.8%
6	185	8.6%
8	156	7.3%
0	150	7.0%
7	44	2.0%
9	17	0.8%

Lowercase Letter

Value	Count	Frequency (%)
e	2922	25.0%
r	1948	16.7%
h	974	8.3%
o	974	8.3%
u	974	8.3%
s	974	8.3%
p	974	8.3%
w	974	8.3%
k	974	8.3%

Space Separator

Value	Count	Frequency (%)
	2922	100.0%

Dash Punctuation

Value	Count	Frequency (%)
–	974	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	11688	65.9%
Common	6044	34.1%

Most frequent character per script

Common

Value	Count	Frequency (%)
	2922	48.3%
–	974	16.1%
4	357	5.9%
3	351	5.8%
2	331	5.5%
1	282	4.7%
5	275	4.5%
6	185	3.1%
8	156	2.6%
0	150	2.5%
Other values (2)	61	1.0%

Latin

Value	Count	Frequency (%)
e	2922	25.0%
r	1948	16.7%
h	974	8.3%
o	974	8.3%
u	974	8.3%
s	974	8.3%
p	974	8.3%
w	974	8.3%
k	974	8.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	16758	94.5%
Punctuation	974	5.5%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	2922	17.4%
	2922	17.4%
r	1948	11.6%
h	974	5.8%
o	974	5.8%
u	974	5.8%
s	974	5.8%
p	974	5.8%
w	974	5.8%
k	974	5.8%
Other values (10)	2148	12.8%

Punctuation

Value	Count	Frequency (%)
–	974	100.0%

duration
Text

Distinct	123
Distinct (%)	2.6%
Missing	623
Missing (%)	11.8%
Memory size	41.3 KiB

Length

Max length	18
Median length	3
Mean length	4.7178218
Min length	3

Characters and Unicode

Total characters	21919
Distinct characters	16
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	33 ?
Unique (%)	0.7%

Sample

1st row	2 Weeks
2nd row	7 Weeks
3rd row	12 Weeks
4th row	13 Weeks
5th row	4 Weeks

Value	Count	Frequency (%)
weeks	974	17.3%
1.0	606	10.8%
1.5	506	9.0%
2.0	419	7.5%
2.5	269	4.8%
3.0	248	4.4%
4	194	3.5%
6	187	3.3%
3.5	182	3.2%
5	148	2.6%
Other values (114)	1887	33.6%

Most occurring characters

Value	Count	Frequency (%)
.	3672	16.8%
0	2335	10.7%
6	2191	10.0%
3	2178	9.9%
5	2030	9.3%
e	1948	8.9%
1	1654	7.5%
	974	4.4%
W	974	4.4%
k	974	4.4%
Other values (6)	2989	13.6%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	12403	56.6%
Lowercase Letter	3896	17.8%
Other Punctuation	3672	16.8%
Space Separator	974	4.4%
Uppercase Letter	974	4.4%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	2335	18.8%
6	2191	17.7%
3	2178	17.6%
5	2030	16.4%
1	1654	13.3%
2	841	6.8%
4	532	4.3%
7	349	2.8%
8	214	1.7%
9	79	0.6%

Lowercase Letter

Value	Count	Frequency (%)
e	1948	50.0%
k	974	25.0%
s	974	25.0%

Other Punctuation

Value	Count	Frequency (%)
.	3672	100.0%

Space Separator

Value	Count	Frequency (%)
	974	100.0%

Uppercase Letter

Value	Count	Frequency (%)
W	974	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	17049	77.8%
Latin	4870	22.2%

Most frequent character per script

Common

Value	Count	Frequency (%)
.	3672	21.5%
0	2335	13.7%
6	2191	12.9%
3	2178	12.8%
5	2030	11.9%
1	1654	9.7%
	974	5.7%
2	841	4.9%
4	532	3.1%
7	349	2.0%
Other values (2)	293	1.7%

Latin

Value	Count	Frequency (%)
e	1948	40.0%
W	974	20.0%
k	974	20.0%
s	974	20.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	21919	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
.	3672	16.8%
0	2335	10.7%
6	2191	10.0%
3	2178	9.9%
5	2030	9.3%
e	1948	8.9%
1	1654	7.5%
	974	4.4%
W	974	4.4%
k	974	4.4%
Other values (6)	2989	13.6%

price
Real number (ℝ)

HIGH CORRELATION MISSING

Distinct	47
Distinct (%)	4.8%
Missing	4295
Missing (%)	81.5%
Infinite	0
Infinite (%)	0.0%
Mean	227.40452

Minimum	5
Maximum	39960
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	41.3 KiB

Quantile statistics

Minimum	5
5-th percentile	25
Q1	49
median	79
Q3	149
95-th percentile	249
Maximum	39960
Range	39955
Interquartile range (IQR)	100

Descriptive statistics

Standard deviation	1891.6947
Coefficient of variation (CV)	8.3186328
Kurtosis	324.84395
Mean	227.40452
Median Absolute Deviation (MAD)	30
Skewness	17.630244
Sum	221492
Variance	3578508.8
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=47)

Value	Count	Frequency (%)
49	270	5.1%
99	136	2.6%
50	96	1.8%
199	85	1.6%
149	78	1.5%
25	49	0.9%
139	33	0.6%
150	30	0.6%
249	26	0.5%
79	21	0.4%
Other values (37)	150	2.8%
(Missing)	4295	81.5%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
5	7	0.1%
10	1	< 0.1%
15	1	< 0.1%
19	2	< 0.1%
25	49	0.9%
29	15	0.3%
30	1	< 0.1%
39	13	0.2%
40	2	< 0.1%
49	270	5.1%

Value	Count	Frequency (%)
39960	1	< 0.1%
29970	2	< 0.1%
4999	4	0.1%
4990	1	< 0.1%
450	1	< 0.1%
399	2	< 0.1%
375	1	< 0.1%
350	3	0.1%
300	3	0.1%
299	9	0.2%

description
Text

Distinct	932
Distinct (%)	99.8%
Missing	4335
Missing (%)	82.3%
Memory size	41.3 KiB

Length

Max length	10163
Median length	1365
Mean length	1210.4839
Min length	137

Characters and Unicode

Total characters	1130592
Distinct characters	1113
Distinct categories	21 ?
Distinct scripts	7 ?
Distinct blocks	11 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	931 ?
Unique (%)	99.7%

Sample

1st row	Designed for those who are new to elearning, this course will prepare you with strategies to be a successful online learner.The edX learning design team has curated some of the most powerful, science-backed techniques which you can start using right away and on any learning platform.The Verified Certificate for this course is free. Use the following coupon code before September 1, 2020 to upgrade at no cost to you: Y5ZADM5NU2AN5JU7This course will help you answer the following questions:
2nd row	This course aims to teach everyone the basics of programming computers using Python. We cover the basics of how one constructs a program from a series of simple instructions in Python. The course has no pre-requisites and avoids all but the simplest mathematics. Anyone with moderate computer experience should be able to master the materials in this course. This course will cover Chapters 1-5 of the textbook "Python for Everybody". Once a student completes this course, they will be ready to take more advanced programming courses. This course covers Python 3.
3rd row	This is CS50x , Harvard University's introduction to the intellectual enterprises of computer science and the art of programming for majors and non-majors alike, with or without prior programming experience. An entry-level course taught by David J. Malan, CS50x teaches students how to think algorithmically and solve problems efficiently. Topics include abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development. Languages include C, Python, SQL, and JavaScript plus CSS and HTML. Problem sets inspired by real-world domains of biology, cryptography, finance, forensics, and gaming. The on-campus version of CS50x , CS50, is Harvard's largest course. Students who earn a satisfactory score on 9 problem sets (i.e., programming assignments) and a final project are eligible for a certificate. This is a self-paced course–you may take CS50x on your own schedule.HarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. HarvardX will take appropriate corrective action in response to violations of the edX honor code, which may include dismissal from the HarvardX course; revocation of any certificates received for the HarvardX course; or other remedies as circumstances warrant. No refunds will be issued in the case of corrective action for such violations. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs.HarvardX pursues the science of learning. By registering as an online learner in an HX course, you will also participate in research about learning. Read our research statement to learn more.Harvard University and HarvardX are committed to maintaining a safe and healthy educational and work environment in which no member of the community is excluded from participation in, denied the benefits of, or subjected to discrimination or harassment in our program. All members of the HarvardX community are expected to abide by Harvard policies on nondiscrimination, including sexual harassment, and the edX Terms of Service. If you have any questions or concerns, please contact harvardx@harvard.edu and/or report your experience through the edX contact form.
4th row	In the last decade, the amount of data available to organizations has reached unprecedented levels. Data is transforming business, social interactions, and the future of our society. In this course, you will learn how to use data and analytics to give an edge to your career and your life. We will examine real world examples of how analytics have been used to significantly improve a business or industry. These examples include Moneyball, eHarmony, the Framingham Heart Study, Twitter, IBM Watson, and Netflix. Through these examples and many more, we will teach you the following analytics methods: linear regression, logistic regression, trees, text analytics, clustering, visualization, and optimization. We will be using the statistical software R to build models and work with data. The contents of this course are essentially the same as those of the corresponding MIT class (The Analytics Edge). It is a challenging class, but it will enable you to apply analytics to real-world applications.The class will consist of lecture videos, which are broken into small pieces, usually between 4 and 8 minutes each. After each lecture piece, we will ask you a "quick question" to assess your understanding of the material. There will also be a recitation, in which one of the teaching assistants will go over the methods introduced with a new example and data set. Each week will have a homework assignment that involves working in R or LibreOffice with various data sets. (R is a free statistical and computing software environment we'll use in the course. See the Software FAQ below for more info). At the end of the class there will be a final exam, which will be similar to the homework assignments.
5th row	Begin your journey in a new career in marketing analytics. Learn about powerful strategies and methodology, starting with identifying market trends and metrics used to measure marketing success.In this marketing course, you will learn how to execute market sizing, identify market trends, and predict future conditions.This course is taught by Stephan Sorger who has held leadership roles in marketing and product development at companies such as Oracle, 3Com and NASA. He has also taught for over a decade at UC Berkeley Extension and is the author of two widely adopted marketing textbooks. This course will equip you with the knowledge and skills necessary to immediately see practical benefits in the workplace.Analytics-based marketing is increasingly important in determining a company’s spending and ROI. Many entry-level positions in marketing now require some basic level of knowledge in this rapidly growing field.

Value	Count	Frequency (%)
the	7102	4.1%
and	6425	3.8%
of	4585	2.7%
to	4378	2.6%
a	3053	1.8%
in	2779	1.6%
de	2645	1.5%
will	2178	1.3%
you	2176	1.3%
course	2159	1.3%
Other values (19110)	133715	78.1%

Most occurring characters

Value	Count	Frequency (%)
	170244	15.1%
e	108751	9.6%
a	76963	6.8%
o	73361	6.5%
i	70503	6.2%
t	69170	6.1%
n	68807	6.1%
s	66279	5.9%
r	59858	5.3%
l	42492	3.8%
Other values (1103)	324164	28.7%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	905179	80.1%
Space Separator	170251	15.1%
Uppercase Letter	22115	2.0%
Other Punctuation	20619	1.8%
Other Letter	5323	0.5%
Decimal Number	2381	0.2%
Dash Punctuation	2179	0.2%
Final Punctuation	749	0.1%
Close Punctuation	671	0.1%
Open Punctuation	659	0.1%
Other values (11)	466	< 0.1%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
的	237	4.5%
学	109	2.0%
和	53	1.0%
影	52	1.0%
本	52	1.0%
程	50	0.9%
人	49	0.9%
中	49	0.9%
文	45	0.8%
生	44	0.8%
Other values (942)	4583	86.1%

Lowercase Letter

Value	Count	Frequency (%)
e	108751	12.0%
a	76963	8.5%
o	73361	8.1%
i	70503	7.8%
t	69170	7.6%
n	68807	7.6%
s	66279	7.3%
r	59858	6.6%
l	42492	4.7%
c	38989	4.3%
Other values (39)	230006	25.4%

Uppercase Letter

Value	Count	Frequency (%)
T	2744	12.4%
I	1762	8.0%
A	1610	7.3%
S	1552	7.0%
C	1508	6.8%
M	1378	6.2%
E	1316	6.0%
P	1250	5.7%
D	1032	4.7%
W	921	4.2%
Other values (22)	7042	31.8%

Other Punctuation

Value	Count	Frequency (%)
,	9646	46.8%
.	7436	36.1%
:	769	3.7%
?	546	2.6%
'	486	2.4%
"	257	1.2%
;	226	1.1%
/	218	1.1%
，	185	0.9%
!	151	0.7%
Other values (16)	699	3.4%

Decimal Number

Value	Count	Frequency (%)
0	559	23.5%
1	478	20.1%
2	421	17.7%
3	201	8.4%
5	179	7.5%
4	127	5.3%
9	114	4.8%
8	113	4.7%
7	101	4.2%
6	88	3.7%

Close Punctuation

Value	Count	Frequency (%)
)	646	96.3%
》	13	1.9%
）	9	1.3%
}	1	0.1%
」	1	0.1%
]	1	0.1%

Open Punctuation

Value	Count	Frequency (%)
(	634	96.2%
《	13	2.0%
（	9	1.4%
{	1	0.2%
「	1	0.2%
[	1	0.2%

Dash Punctuation

Value	Count	Frequency (%)
-	1975	90.6%
–	105	4.8%
—	96	4.4%
―	2	0.1%
‑	1	< 0.1%

Math Symbol

Value	Count	Frequency (%)
+	9	56.2%
~	4	25.0%
=	2	12.5%
<	1	6.2%

Final Punctuation

Value	Count	Frequency (%)
’	621	82.9%
”	121	16.2%
»	7	0.9%

Initial Punctuation

Value	Count	Frequency (%)
“	124	75.6%
‘	33	20.1%
«	7	4.3%

Other Symbol

Value	Count	Frequency (%)
®	48	54.5%
●	36	40.9%
™	4	4.5%

Space Separator

Value	Count	Frequency (%)
	170244	> 99.9%
	7	< 0.1%

Control

Value	Count	Frequency (%)
	144	98.6%
	2	1.4%

Currency Symbol

Value	Count	Frequency (%)
$	21	95.5%
€	1	4.5%

Modifier Letter

Value	Count	Frequency (%)
ー	3	75.0%
々	1	25.0%

Modifier Symbol

Value	Count	Frequency (%)
`	1	50.0%
^	1	50.0%

Connector Punctuation

Value	Count	Frequency (%)
_	13	100.0%

Private Use

Value	Count	Frequency (%)
	6	100.0%

Nonspacing Mark

Value	Count	Frequency (%)
́	3	100.0%

Format

Value	Count	Frequency (%)
	2	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	927294	82.0%
Common	197965	17.5%
Han	4993	0.4%
Hiragana	313	< 0.1%
Katakana	18	< 0.1%
Unknown	6	< 0.1%
Inherited	3	< 0.1%

Most frequent character per script

Han

Value	Count	Frequency (%)
的	237	4.7%
学	109	2.2%
和	53	1.1%
影	52	1.0%
本	52	1.0%
程	50	1.0%
人	49	1.0%
中	49	1.0%
文	45	0.9%
生	44	0.9%
Other values (888)	4253	85.2%

Latin

Value	Count	Frequency (%)
e	108751	11.7%
a	76963	8.3%
o	73361	7.9%
i	70503	7.6%
t	69170	7.5%
n	68807	7.4%
s	66279	7.1%
r	59858	6.5%
l	42492	4.6%
c	38989	4.2%
Other values (71)	252121	27.2%

Common

Value	Count	Frequency (%)
	170244	86.0%
,	9646	4.9%
.	7436	3.8%
-	1975	1.0%
:	769	0.4%
)	646	0.3%
(	634	0.3%
’	621	0.3%
0	559	0.3%
?	546	0.3%
Other values (67)	4889	2.5%

Hiragana

Value	Count	Frequency (%)
る	20	6.4%
を	20	6.4%
の	20	6.4%
で	20	6.4%
と	16	5.1%
に	16	5.1%
な	12	3.8%
す	12	3.8%
は	12	3.8%
て	12	3.8%
Other values (35)	153	48.9%

Katakana

Value	Count	Frequency (%)
ス	4	22.2%
ク	3	16.7%
コ	3	16.7%
ッ	2	11.1%
リ	1	5.6%
ト	1	5.6%
ピ	1	5.6%
ラ	1	5.6%
イ	1	5.6%
ド	1	5.6%

Unknown

Value	Count	Frequency (%)
	6	100.0%

Inherited

Value	Count	Frequency (%)
́	3	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1119236	99.0%
CJK	4992	0.4%
None	4745	0.4%
Punctuation	1219	0.1%
Hiragana	313	< 0.1%
Katakana	37	< 0.1%
Geometric Shapes	36	< 0.1%
PUA	6	< 0.1%
Letterlike Symbols	4	< 0.1%
Diacriticals	3	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	170244	15.2%
e	108751	9.7%
a	76963	6.9%
o	73361	6.6%
i	70503	6.3%
t	69170	6.2%
n	68807	6.1%
s	66279	5.9%
r	59858	5.3%
l	42492	3.8%
Other values (84)	312808	27.9%

None

Value	Count	Frequency (%)
á	1137	24.0%
ó	1133	23.9%
é	648	13.7%
í	605	12.8%
，	185	3.9%
ñ	159	3.4%
ú	149	3.1%
、	117	2.5%
¿	97	2.0%
。	89	1.9%
Other values (39)	426	9.0%

Punctuation

Value	Count	Frequency (%)
’	621	50.9%
“	124	10.2%
”	121	9.9%
–	105	8.6%
•	100	8.2%
—	96	7.9%
‘	33	2.7%
…	14	1.1%
	2	0.2%
―	2	0.2%

CJK

Value	Count	Frequency (%)
的	237	4.7%
学	109	2.2%
和	53	1.1%
影	52	1.0%
本	52	1.0%
程	50	1.0%
人	49	1.0%
中	49	1.0%
文	45	0.9%
生	44	0.9%
Other values (887)	4252	85.2%

Geometric Shapes

Value	Count	Frequency (%)
●	36	100.0%

Hiragana

Value	Count	Frequency (%)
る	20	6.4%
を	20	6.4%
の	20	6.4%
で	20	6.4%
と	16	5.1%
に	16	5.1%
な	12	3.8%
す	12	3.8%
は	12	3.8%
て	12	3.8%
Other values (35)	153	48.9%

Katakana

Value	Count	Frequency (%)
・	16	43.2%
ス	4	10.8%
ク	3	8.1%
ー	3	8.1%
コ	3	8.1%
ッ	2	5.4%
リ	1	2.7%
ト	1	2.7%
ピ	1	2.7%
ラ	1	2.7%
Other values (2)	2	5.4%

PUA

Value	Count	Frequency (%)
	6	100.0%

Letterlike Symbols

Value	Count	Frequency (%)
™	4	100.0%

Diacriticals

Value	Count	Frequency (%)
́	3	100.0%

Currency Symbols

Value	Count	Frequency (%)
€	1	100.0%

curriculum
Text

Distinct	412
Distinct (%)	98.8%
Missing	4852
Missing (%)	92.1%
Memory size	41.3 KiB

Length

Max length	5057
Median length	886
Mean length	877.70024
Min length	4

Characters and Unicode

Total characters	366001
Distinct characters	366
Distinct categories	19 ?
Distinct scripts	5 ?
Distinct blocks	7 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	409 ?
Unique (%)	98.1%

Sample

1st row	Welcome - We start with opportunities to meet your instructors and fellow learners. Self-care for Learning - In this module, we then explore baseline self-care strategies that will help you maintain a healthy mind for effective online learning, the connections between memory and learning, and the importance of sleep. Space, Time, and Technology - In this module we address the challenges involved with creating a space for learning, including managing your technology. We also cover techniques for time management and keeping a routine. Learning Strategies - This module will help you get the most out your online learning experience. We cover effective study strategies and practices, making plans and setting priorities, and practicing self-regulation skills. Communication and Community - In this module, we talk about the importance of social learning. We cover strategies for communication, collaborating, and building connections with your instructors and fellow learners. What's Next? - Get started learning online!
2nd row	MODULE 1: INTRODUCTION TO TEAMS Focuses on recognizing the distinction between groups and teams; developing an understanding of your own group/team loyalties and priorities and considering the building blocks for high-performing teams. MODULE 2: MOTIVATING AND ENGAGING PEOPLE Examines what motivates and engages people at work, developing strategies for improving motivation and engagement in your employees, and what motivates and drives your own behavior. MODULE 3: MANAGING WORK RELATIONSHIPS Considers the nature of your work relationships, how to develop strategies for strengthening employee trust and attachment to the group, how to manage those particularly difficult people at work, and recognizing the importance of external stakeholder relationships. MODULE 4: LEADING TEAMS FOR EXECUTION Considers how to recognize the ingredients for team execution, how to identify challenges in team communication and coordination, and how to develop strategies to enhance team communication/coordination. MODULE 5: LEADING TEAMS TO SOLVE PROBLEMS Focuses on recognizing the value of openness and inclusion for problem solving and creative teams, how to develop better problem solving in your teams - both in execution and in team culture. MODULE 6: WHEN GOOD TEAMS FAIL (PART 1): TOO MUCH CONFLICT! Examines the causes and consequences of serious and escalating conflict, developing strategies for preventing serious/escalating conflict, and developing competencies for resolving serious conflict. MODULE 7: WHEN GOOD TEAMS FAIL (PART 2): TOO MUCH COHESION! Enables you to recognize the warning signs that your team is too cohesive and develop strategies for promoting productive conflict in teams. MODULE 8: BRINGING DIVIDED GROUPS TOGETHER Enables you to recognize patterns and implications of intergroup behavior in your organization, develop strategies for bridging organizational silos and identify the steps for building an inclusive organizational identity. MODULE 9: ORGANIZATIONAL CULTURE Enables you to identify the impact of organizational culture and its (non) alignment to a broader organizational strategy, and recognize points of potential influence when trying to change organizational culture. MODULE 10: BRINGING IT TOGETHER: ANALYZING AND DEVELOPING YOUR TEAM Develops skills in recognizing the strengths and weaknesses of your team, critically analyzing the processes affecting team effectiveness, and considering ways to further develop your team for high-performance.
3rd row	Module 1: Mental fitnessBy the end of this module, you will be able to:Module 2: Cognitive-behavioural strategies to increase mental fitnessBy the end of this module, you will be able to:
4th row	Week 1: Six Sigma Introduction Introduction to the Six Sigma Methodology and the DMAIC process improvement cycle. Understand the contributors to the cost of quality. Discuss the difference between defects and defectives in a process and how to calculate process yield, including a comparison of processes of different complexity using the metric DPMO.Week 2: DEFINE - Defining the Problem Discuss how to understand customer expectations, using the Kano Model to categorize quality characteristics. Start the first and difficult task of a Six Sigma project, Defining the Problem, and review the key content in a Project Charter.Week 3: MEASURE - Statistics Review Review of random variables and probability distributions used commonly in quality engineering, such as Binomial, Poisson, and Exponential. Cover descriptive statistics, emphasizing the importance of clearly communicating the results of your project.Week 4: MEASURE - Normal Distribution Learn the characteristics of the Normal Distribution and how to use the Standard Normal to calculate probabilities related to normally distributed variables. Cover the Central Limit Theorem, and how it relates to sampling theory.Week 5: MEASURE - Process Mapping Introduce Process Mapping, including SIPOC and Value Stream Mapping. We identify the Critical-to-Quality characteristic for a Six Sigma projectWeek 6: MEASURE - Measurement System Analysis Learn the basics of Measurement Theory and Sampling Plans, including Precision, Accuracy, Linearity, Bias, Stability, Gage Repeatability & ReproducibilityWeek 7: MEASURE - Process Capability Introduction to Process Capability and the metrics CP/CPK for establishing our baseline process performance.Week 8: Quality Topics and Course Summary Cover the basics of Tolerance Design and the risk assessment tool failure Mode and Effects Analysis (FMEA). Review the complete Six Sigma Roadmap before summarizing and closing the course.
5th row	1 Basic Counting2 Advanced Counting3 Basic Probability4 Expected Value5 Conditional Probability6 Bernoulli Trials7 The Normal Distribution

Value	Count	Frequency (%)
and	2262	4.1%
the	2092	3.8%
of	1444	2.6%
to	1075	2.0%
de	879	1.6%
in	708	1.3%
a	654	1.2%
	598	1.1%
week	578	1.1%
module	519	0.9%
Other values (10621)	44061	80.3%

Most occurring characters

Value	Count	Frequency (%)
	53080	14.5%
e	33703	9.2%
a	23836	6.5%
i	22876	6.3%
n	22687	6.2%
o	22616	6.2%
t	21655	5.9%
s	19038	5.2%
r	16642	4.5%
l	13008	3.6%
Other values (356)	116860	31.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	276499	75.5%
Space Separator	53081	14.5%
Uppercase Letter	18516	5.1%
Other Punctuation	8771	2.4%
Decimal Number	4335	1.2%
Control	1601	0.4%
Dash Punctuation	1104	0.3%
Other Letter	1013	0.3%
Close Punctuation	372	0.1%
Open Punctuation	362	0.1%
Other values (9)	347	0.1%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
ا	115	11.4%
ل	84	8.3%
م	48	4.7%
ة	33	3.3%
ت	33	3.3%
و	33	3.3%
ي	32	3.2%
ن	27	2.7%
ر	24	2.4%
ع	23	2.3%
Other values (221)	561	55.4%

Lowercase Letter

Value	Count	Frequency (%)
e	33703	12.2%
a	23836	8.6%
i	22876	8.3%
n	22687	8.2%
o	22616	8.2%
t	21655	7.8%
s	19038	6.9%
r	16642	6.0%
l	13008	4.7%
c	11996	4.3%
Other values (29)	68442	24.8%

Uppercase Letter

Value	Count	Frequency (%)
M	1610	8.7%
T	1505	8.1%
S	1415	7.6%
C	1400	7.6%
I	1365	7.4%
W	1296	7.0%
E	1206	6.5%
P	1104	6.0%
A	1075	5.8%
D	1018	5.5%
Other values (22)	5522	29.8%

Other Punctuation

Value	Count	Frequency (%)
.	3186	36.3%
:	2294	26.2%
,	2184	24.9%
?	264	3.0%
;	138	1.6%
*	129	1.5%
'	120	1.4%
¿	81	0.9%
/	80	0.9%
&	77	0.9%
Other values (15)	218	2.5%

Decimal Number

Value	Count	Frequency (%)
1	1018	23.5%
2	826	19.1%
3	680	15.7%
4	563	13.0%
5	385	8.9%
6	260	6.0%
0	218	5.0%
7	162	3.7%
8	132	3.0%
9	91	2.1%

Nonspacing Mark

Value	Count	Frequency (%)
ّ	7	58.3%
ُ	3	25.0%
َ	1	8.3%
ً	1	8.3%

Dash Punctuation

Value	Count	Frequency (%)
-	969	87.8%
–	105	9.5%
—	30	2.7%

Close Punctuation

Value	Count	Frequency (%)
)	351	94.4%
）	18	4.8%
]	3	0.8%

Open Punctuation

Value	Count	Frequency (%)
(	341	94.2%
（	18	5.0%
[	3	0.8%

Math Symbol

Value	Count	Frequency (%)
\|	6	50.0%
+	4	33.3%
−	2	16.7%

Space Separator

Value	Count	Frequency (%)
	53080	> 99.9%
	1	< 0.1%

Control

Value	Count	Frequency (%)
	1585	99.0%
	16	1.0%

Final Punctuation

Value	Count	Frequency (%)
’	133	70.7%
”	55	29.3%

Initial Punctuation

Value	Count	Frequency (%)
“	55	96.5%
‘	2	3.5%

Connector Punctuation

Value	Count	Frequency (%)
_	40	100.0%

Other Symbol

Value	Count	Frequency (%)
●	30	100.0%

Format

Value	Count	Frequency (%)
	4	100.0%

Currency Symbol

Value	Count	Frequency (%)
$	3	100.0%

Modifier Symbol

Value	Count	Frequency (%)
^	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	295019	80.6%
Common	69961	19.1%
Arabic	643	0.2%
Han	366	0.1%
Inherited	12	< 0.1%

Most frequent character per script

Han

Value	Count	Frequency (%)
的	12	3.3%
心	10	2.7%
将	8	2.2%
第	8	2.2%
脏	7	1.9%
我	7	1.9%
和	6	1.6%
理	5	1.4%
周	5	1.4%
们	5	1.4%
Other values (186)	293	80.1%

Latin

Value	Count	Frequency (%)
e	33703	11.4%
a	23836	8.1%
i	22876	7.8%
n	22687	7.7%
o	22616	7.7%
t	21655	7.3%
s	19038	6.5%
r	16642	5.6%
l	13008	4.4%
c	11996	4.1%
Other values (62)	86962	29.5%

Common

Value	Count	Frequency (%)
	53080	75.9%
.	3186	4.6%
:	2294	3.3%
,	2184	3.1%
	1585	2.3%
1	1018	1.5%
-	969	1.4%
2	826	1.2%
3	680	1.0%
4	563	0.8%
Other values (50)	3576	5.1%

Arabic

Value	Count	Frequency (%)
ا	115	17.9%
ل	84	13.1%
م	48	7.5%
ة	33	5.1%
ت	33	5.1%
و	33	5.1%
ي	32	5.0%
ن	27	4.2%
ر	24	3.7%
ع	23	3.6%
Other values (24)	191	29.7%

Inherited

Value	Count	Frequency (%)
ّ	7	58.3%
ُ	3	25.0%
َ	1	8.3%
ً	1	8.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	362653	99.1%
None	1833	0.5%
Arabic	669	0.2%
Punctuation	448	0.1%
CJK	366	0.1%
Geometric Shapes	30	< 0.1%
Math Operators	2	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	53080	14.6%
e	33703	9.3%
a	23836	6.6%
i	22876	6.3%
n	22687	6.3%
o	22616	6.2%
t	21655	6.0%
s	19038	5.2%
r	16642	4.6%
l	13008	3.6%
Other values (78)	113512	31.3%

None

Value	Count	Frequency (%)
ó	796	43.4%
á	271	14.8%
é	222	12.1%
í	197	10.7%
ñ	85	4.6%
¿	81	4.4%
ú	25	1.4%
§	22	1.2%
）	18	1.0%
（	18	1.0%
Other values (21)	98	5.3%

Punctuation

Value	Count	Frequency (%)
’	133	29.7%
–	105	23.4%
•	63	14.1%
”	55	12.3%
“	55	12.3%
—	30	6.7%
	4	0.9%
‘	2	0.4%
…	1	0.2%

Arabic

Value	Count	Frequency (%)
ا	115	17.2%
ل	84	12.6%
م	48	7.2%
ة	33	4.9%
ت	33	4.9%
و	33	4.9%
ي	32	4.8%
ن	27	4.0%
ر	24	3.6%
ع	23	3.4%
Other values (30)	217	32.4%

Geometric Shapes

Value	Count	Frequency (%)
●	30	100.0%

CJK

Value	Count	Frequency (%)
的	12	3.3%
心	10	2.7%
将	8	2.2%
第	8	2.2%
脏	7	1.9%
我	7	1.9%
和	6	1.6%
理	5	1.4%
周	5	1.4%
们	5	1.4%
Other values (186)	293	80.1%

Math Operators

Value	Count	Frequency (%)
−	2	100.0%

paid
Boolean

HIGH CORRELATION MISSING

Distinct	2
Distinct (%)	< 0.1%
Missing	623
Missing (%)	11.8%
Memory size	41.3 KiB

True	3362
False	1284
(Missing)	623

Common Values (Table)
Common Values (Plot)

Value	Count	Frequency (%)
True	3362	63.8%
False	1284	24.4%
(Missing)	623	11.8%

n_reviews
Real number (ℝ)

HIGH CORRELATION MISSING ZEROS

Distinct	511
Distinct (%)	13.9%
Missing	1597
Missing (%)	30.3%
Infinite	0
Infinite (%)	0.0%
Mean	156.37146

Minimum	0
Maximum	27445
Zeros	284
Zeros (%)	5.4%
Negative	0
Negative (%)	0.0%
Memory size	41.3 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	4
median	18
Q3	67
95-th percentile	485.15
Maximum	27445
Range	27445
Interquartile range (IQR)	63

Descriptive statistics

Standard deviation	936.17865
Coefficient of variation (CV)	5.9868895
Kurtosis	398.54493
Mean	156.37146
Median Absolute Deviation (MAD)	16
Skewness	17.803799
Sum	574196
Variance	876430.46
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	284	5.4%
1	184	3.5%
2	166	3.2%
3	160	3.0%
4	127	2.4%
5	109	2.1%
6	101	1.9%
8	82	1.6%
10	78	1.5%
11	76	1.4%
Other values (501)	2305	43.7%
(Missing)	1597	30.3%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	284	5.4%
1	184	3.5%
2	166	3.2%
3	160	3.0%
4	127	2.4%
5	109	2.1%
6	101	1.9%
7	73	1.4%
8	82	1.6%
9	71	1.3%

Value	Count	Frequency (%)
27445	1	< 0.1%
22412	1	< 0.1%
19649	1	< 0.1%
16976	1	< 0.1%
15117	1	< 0.1%
11580	1	< 0.1%
11123	1	< 0.1%
8629	1	< 0.1%
8341	1	< 0.1%
7676	1	< 0.1%

n_lectures
Real number (ℝ)

HIGH CORRELATION MISSING

Distinct	229
Distinct (%)	6.2%
Missing	1597
Missing (%)	30.3%
Infinite	0
Infinite (%)	0.0%
Mean	40.140251

Minimum	0
Maximum	779
Zeros	1
Zeros (%)	< 0.1%
Negative	0
Negative (%)	0.0%
Memory size	41.3 KiB

Quantile statistics

Minimum	0
5-th percentile	7
Q1	15
median	25
Q3	46
95-th percentile	119
Maximum	779
Range	779
Interquartile range (IQR)	31

Descriptive statistics

Standard deviation	50.417102
Coefficient of variation (CV)	1.2560236
Kurtosis	36.744899
Mean	40.140251
Median Absolute Deviation (MAD)	13
Skewness	4.8701263
Sum	147395
Variance	2541.8842
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
12	121	2.3%
15	109	2.1%
13	107	2.0%
14	105	2.0%
11	104	2.0%
16	99	1.9%
9	99	1.9%
20	94	1.8%
19	90	1.7%
24	88	1.7%
Other values (219)	2656	50.4%
(Missing)	1597	30.3%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	1	< 0.1%
4	2	< 0.1%
5	54	1.0%
6	63	1.2%
7	77	1.5%
8	83	1.6%
9	99	1.9%
10	87	1.7%
11	104	2.0%
12	121	2.3%

Value	Count	Frequency (%)
779	1	< 0.1%
544	1	< 0.1%
536	1	< 0.1%
527	1	< 0.1%
491	1	< 0.1%
462	1	< 0.1%
460	1	< 0.1%
458	1	< 0.1%
454	1	< 0.1%
444	1	< 0.1%

published
Date

Distinct	3672
Distinct (%)	100.0%
Missing	1597
Missing (%)	30.3%
Memory size	41.3 KiB

Minimum	2011-07-09 05:43:31
Maximum	2017-07-06 21:46:30

Histogram

Histogram with fixed size bins (bins=50)

Auto

Heatmap
Table

	n_subscribers	price	n_reviews	n_lectures	mooc	modality	level	subject	language	subtitles	paid
n_subscribers	1.000	0.122	0.784	0.210	0.169	0.000	0.083	0.158	0.000	0.000	0.143
price	0.122	1.000	NaN	NaN	1.000	0.000	0.084	0.000	0.000	0.000	1.000
n_reviews	0.784	NaN	1.000	0.341	1.000	0.000	0.000	0.041	0.000	0.000	0.079
n_lectures	0.210	NaN	0.341	1.000	1.000	0.000	0.061	0.094	0.000	0.000	0.074
mooc	0.169	1.000	1.000	1.000	1.000	1.000	0.999	0.996	1.000	1.000	0.833
modality	0.000	0.000	0.000	0.000	1.000	1.000	0.361	0.080	0.186	0.114	1.000
level	0.083	0.084	0.000	0.061	0.999	0.361	1.000	0.443	0.092	0.109	0.835
subject	0.158	0.000	0.041	0.094	0.996	0.080	0.443	1.000	0.114	0.224	0.830
language	0.000	0.000	0.000	0.000	1.000	0.186	0.092	0.114	1.000	0.937	1.000
subtitles	0.000	0.000	0.000	0.000	1.000	0.114	0.109	0.224	0.937	1.000	1.000
paid	0.143	1.000	0.079	0.074	0.833	1.000	0.835	0.830	1.000	1.000	1.000

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

First rows
Last rows

	title	institution	url	id	mooc	summary	n_subscribers	modality	instructors	level	subject	language	subtitles	effort	duration	price	description	curriculum	paid	n_reviews	n_lectures	published
0	Machine Learning	Stanford University	https://www.coursera.org/learn/machine-learning	machine-learning	coursera	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	Indigenous Canada	University of Alberta	https://www.coursera.org/learn/indigenous-canada	indigenous-canada	coursera	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	The Science of Well-Being	Yale University	https://www.coursera.org/learn/the-science-of-well-being	the-science-of-well-being	coursera	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	Technical Support Fundamentals	Google	https://www.coursera.org/learn/technical-support-fundamentals	technical-support-fundamentals	coursera	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	Become a CBRS Certified Professional Installer by Google	Google - Spectrum Sharing	https://www.coursera.org/learn/google-cbrs-cpi-training	google-cbrs-cpi-training	coursera	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
5	Financial Markets	Yale University	https://www.coursera.org/learn/financial-markets-global	financial-markets-global	coursera	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
6	Introduction to Psychology	Yale University	https://www.coursera.org/learn/introduction-psychology	introduction-psychology	coursera	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
7	Programming for Everybody (Getting Started with Python)	University of Michigan	https://www.coursera.org/learn/python	python	coursera	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
8	The Bits and Bytes of Computer Networking	Google	https://www.coursera.org/learn/computer-networking	computer-networking	coursera	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
9	AI For Everyone	DeepLearning.AI	https://www.coursera.org/learn/ai-for-everyone	ai-for-everyone	coursera	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	title	institution	url	id	mooc	summary	n_subscribers	modality	instructors	level	subject	language	subtitles	effort	duration	price	description	curriculum	paid	n_reviews	n_lectures	published
5259	A how to guide in HTML	NaN	https://www.udemy.com/a-how-to-guide-in-html/	270976	udemy	NaN	7318.0	NaN	NaN	Beginner Level	Web Development	NaN	NaN	NaN	0.5833333333333334	NaN	NaN	NaN	False	205.0	8.0	2014-08-10T20:19:10Z
5260	Building Better APIs with GraphQL	NaN	https://www.udemy.com/building-better-apis-with-graphql/	679992	udemy	NaN	555.0	NaN	NaN	All Levels	Web Development	NaN	NaN	NaN	2.5	NaN	NaN	NaN	True	89.0	16.0	2015-11-29T22:02:02Z
5261	Learn Grunt with Examples: Automate Your Front End Workflow	NaN	https://www.udemy.com/learn-grunt-automate-your-front-end-workflow/	330900	udemy	NaN	496.0	NaN	NaN	All Levels	Web Development	NaN	NaN	NaN	1.0	NaN	NaN	NaN	True	113.0	17.0	2014-12-19T21:38:54Z
5262	Build A Stock Downloader With Visual Studio 2015 And C#	NaN	https://www.udemy.com/csharpyahoostockdownloader/	667122	udemy	NaN	436.0	NaN	NaN	Intermediate Level	Web Development	NaN	NaN	NaN	1.5	NaN	NaN	NaN	True	36.0	22.0	2015-11-19T17:22:47Z
5263	jQuery UI in Action: Build 5 jQuery UI Projects	NaN	https://www.udemy.com/jquery-ui-practical-build-jquery-ui-projects/	865438	udemy	NaN	382.0	NaN	NaN	All Levels	Web Development	NaN	NaN	NaN	15.5	NaN	NaN	NaN	True	28.0	140.0	2016-10-10T22:00:32Z
5264	Learn jQuery from Scratch - Master of JavaScript library	NaN	https://www.udemy.com/easy-jquery-for-beginner-learn-from-scratch-step-by-step/	775618	udemy	NaN	1040.0	NaN	NaN	All Levels	Web Development	NaN	NaN	NaN	2.0	NaN	NaN	NaN	True	14.0	21.0	2016-06-14T17:36:46Z
5265	How To Design A WordPress Website With No Coding At All	NaN	https://www.udemy.com/how-to-make-a-wordpress-website-course/	1088178	udemy	NaN	306.0	NaN	NaN	Beginner Level	Web Development	NaN	NaN	NaN	3.5	NaN	NaN	NaN	True	3.0	42.0	2017-03-10T22:24:30Z
5266	Learn and Build using Polymer	NaN	https://www.udemy.com/learn-and-build-using-polymer/	635248	udemy	NaN	513.0	NaN	NaN	All Levels	Web Development	NaN	NaN	NaN	3.5	NaN	NaN	NaN	True	169.0	48.0	2015-12-30T16:41:42Z
5267	CSS Animations: Create Amazing Effects on Your Website	NaN	https://www.udemy.com/css-animations-create-amazing-effects-on-your-website/	905096	udemy	NaN	300.0	NaN	NaN	All Levels	Web Development	NaN	NaN	NaN	3.0	NaN	NaN	NaN	True	31.0	38.0	2016-08-11T19:06:15Z
5268	Using MODX CMS to Build Websites: A Beginner's Guide	NaN	https://www.udemy.com/using-modx-cms-to-build-websites-a-beginners-guide/	297602	udemy	NaN	901.0	NaN	NaN	Beginner Level	Web Development	NaN	NaN	NaN	2.0	NaN	NaN	NaN	True	36.0	20.0	2014-09-28T19:51:11Z

Overview

Variables

Most occurring characters

Most occurring categories

Most frequent character per category

Other Letter

Lowercase Letter

Uppercase Letter

Other Punctuation

Decimal Number

Nonspacing Mark

Math Symbol

Close Punctuation

Open Punctuation

Dash Punctuation

Space Separator

Other Symbol

Letter Number

Modifier Symbol

Final Punctuation

Modifier Letter

Format

Control

Most occurring scripts

Most frequent character per script

Han

Latin

Common

Katakana

Hiragana

Cyrillic

Thai

Arabic

Hangul

Inherited

Most occurring blocks

Most frequent character per block

ASCII

None

Arabic

Cyrillic

Punctuation

Katakana

Hiragana

CJK

Thai

Number Forms

Letterlike Symbols

Dingbats

VS

Math Operators

Hangul

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Other Punctuation

Space Separator

Dash Punctuation

Open Punctuation

Close Punctuation

Connector Punctuation

Decimal Number

Final Punctuation

Other Number

Most occurring scripts

Most frequent character per script

Latin

Common

Most occurring blocks

Most frequent character per block

ASCII

None

Punctuation

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Decimal Number